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Associate Professor Jaime Valls Miro

Biography

Jaime Valls Miro (Valencia, Spain, 1/11/1969) received the B.Eng. and M.Eng. degrees in Computer Science (Systems Engineering) from the Valencia Polytechnic University (UPV), Valencia (Spain), in 1990 and 1993 respectively. He received his Ph.D. degree from Middlesex University, London (UK) in 1998. His thesis examined the use of full dynamics for trajectory planning and optimal control of industrial manipulators. Before joining UTS, he worked for 5 years as a software and control systems analyst for a London-based company designing ROVs (remotely operated underwater vehicles). Currently, he is an Associate Professor at  the Centre for Autonomous Systems.

Professional

Regular reviewer of technical papers in top international robotic journals (e.g. Journal of Field Robotics, Journal of Systems, Man and Cybernetics - Part B, International Journal of Automation and Control, Sensors) and conferences (e.g. International Conference on Robotics and Automation, International Conference on Intelligent Robots and Systems, International Conference on Field and Service Robotics).

Committee member at various robotics conferences (e.g. Australasian Conference on Robotics and Automation). Conference symposium organiser (e.g. International Conference on Intelligent Sensors, Sensor Networks and Information Processing).

Invited keynote speaker ('Rescue Robots' at the National Emergency Management Conference).

Regularly approached by the general media for expert advice and to participate in the dissemination of robotic sciences for a wider audience (Sydney Morning Herald, ABC, Channel 10, Channel 7).

Image of Jaime Valls Miro
Associate Professor, A/DRsch Ctre for Autonomous Systems
Associate Member, CHT - Centre for Health Technologies
Core Member, CAS - Centre for Autonomous Systems
PhD (Middlesex)
Member, Institute of Electrical and Electronics Engineers
Member, The Institution of Engineering Technology (IET)
 
Phone
+61 2 9514 2967

Research Interests

His research activities include autonomous mobile robot navigation and mapping (in particular in unstructured and challenging scenarios such as Urban Search and Rescue, or USAR), visual SLAM (Simultaneous Localization and Mapping), probabilistic models for cognitive human-robot interaction (HRI), healthcare/rehabilitation robotics, underwater ROVs, robot modelling and control.

Chapters

Martín, F., Valls Miró, J. & Moreno, L. 2014, 'Towards exploiting the advantages of colour in scan matching', pp. 217-231.
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© Springer International Publishing Switzerland 2014. Colour plays an important role in the perception systems of the human beings. In robotics, the development of new sensors has made it possible to obtain colour information together with depth information about the environment. The exploitation of this type of information has become more and more important in numerous tasks. In our recent work, we have developed an evolutionary-based scan matching method. The aim of this work is to modify this method by the introduction of colour properties, taking the first steps in studying how to use colour to improve the scan matching. In particular, we have applied a colour transition detection method based on the delta E divergence between neighbours in a scan. Our algorithm has been tested in a real environment and significant conclusions have been reached.

Conferences

Shi, L., Valls Miro, J., Rajalingam, J., Wood, R. & Vitanage, D. 2016, 'High Precision GPS Aided In-pipe Distance Calibration For Satellite Image-based Pipeline Mapping', OZWATER'16 Australia's International Water Conference & Exhibition, OZWATER'16 Australia's International Water Conference & Exhibition, Australian Water Association, Melbourne, pp. 1-8.
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Asset management and pipe condition assessment (CA) activities in the water industry usually require locating buried pipes accurately to minimise inspection and maintenance costs. A typical challenge in practice is locating an anomaly detected by an in-pipe inspection tool from aboveground in order to dig up a pipe for replacement. Accumulated in-pipe errors over longer distances in particular can easily lead to selecting the wrong pipe section for further investigation or exhumation. In fact, some in-pipe CA providers suggest utility personnel dig up a number of sections of pipe around the suggested location so as to ensure finding the target section. In this paper we propose a mechanism to accurately correlate a 3D pipeline profile built from GPS surveying results of aboveground pipeline features with in-pipe chainage distances, so as to establish an accurate link between above-ground GPS coordinates and inpipe distance measurements. This approach naturally characterises and corrects for some of the most prominent in-pipe chainage measurement errors that can lead to uncertainties about the reported location of a buried pipeline from above-ground. The detailed pipeline information can then be projected onto satellite imagery as an accurate easy-to-understand reference for efficient decision making.
Sun, L., Vidal Calleja, T.A. & Valls Miro, J. 2016, 'Gaussian Markov Random Fields for Fusion in Information Form', Proceedings - IEEE International Conference on Robotics and Automation, 2016 IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers (IEEE), Stockholm, Sweden, pp. 1840-1845.
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2.5D maps are preferable for representing the environment owing to compactness. When noisy observations from diverse sensors at different resolutions are available, the problem of 2.5D mapping turns to how to compound the information in an effective and efficient manner. This paper proposes a generic probabilistic framework for fusing efficiently multiple sources of sensor data to generate amendable, high-resolution 2.5D maps. The key idea is to exploit the sparsity of the information matrix. Gaussian Markov Random Fields are employed to learn a prior map, using the conditional independence property between location to obtain a representation of the state. This prior map encoded in information form can then be updated with other sources of data in constant time. Later, mean state vector and variances can be efficiently recovered using numerical methods. The proposed approach allows accurate estimation of 2.5D maps at arbitrary resolution, while incorporating sensor noise and spatial dependency in a statistically reasonable way. We apply the proposed framework to pipe wall thickness mapping and fuse data from two diverse sensors that have different resolutions. Experimental results are compared with three other approaches, showing that, while greatly reducing computation time, the proposed framework is able to capture in large extend the spatial correlation to generate equivalent results to the expensive optimal fusion method in covariance form with a Gaussian Process prior.
Su, D., Vidal Calleja, T.A. & Valls Miro, J. 2016, 'Split Conditional Independent Mapping for Sound Source Localisation with Inverse-Depth Parametrisation', IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 2000-2006.
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In this paper, we propose a framework to map stationary sound sources while simultaneously localise a moving robot. Conventional methods for localisation and sound source mapping rely on a microphone array and either, a proprioceptive sensor only (such as wheel odometry) or an additional exteroceptive sensor (such as cameras or lasers) to get accurately the robot locations. Since odometry drifts over time and sound observations are bearing-only, sparse and extremely noisy, the former can only deal with relatively short trajectories before the whole map drifts. In comparison, the latter can get more accurate trajectory estimation over long distances and a better estimation of the sound source map as a result. However, in most of the work in the literature, trajectory estimation and sound source mapping are treated as uncorrelated, which means an update on the robot trajectory does not propagate properly to the sound source map. In this paper, we proposed an efficient method to correlate robot trajectory with sound source mapping by exploiting the conditional independence property between two maps estimated by two different Simultaneous Localisation and Mapping (SLAM) algorithms running in parallel. In our approach, the first map has the flexibility that can be built with any SLAM algorithm (filtering or optimisation) to estimate robot poses with an exteroceptive sensor. The second map is built by using a filtering-based SLAM algorithm locating all stationary sound sources parametrised with Inverse Depth Parametrisation (IDP). Robot locations used during IDP initialisation are the common features shared between the two SLAM maps, which allow to propagate information accordingly. Comprehensive simulations and experimental results show the effectiveness of the proposed method.
Shi, L., Valls Miro, J., Zhang, T., Vidal Calleja, T., Sun, L. & Dissanayake, G. 2016, 'Constrained Sampling of 2.5D Probabilistic Maps for Augmented Inference', 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.
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Shi, L. & Valls Miro, J. 2016, 'Towards Optimized and Reconstructable Sampling Inspection of Pipe Integrity for Improved Efficiency of NDT', 2016 IWA World Water Congress, Brisbane, Australia.
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Su, D., Nakamura, K., Nakadai, K. & Valls Miro, J. 2016, 'Robust Sound Source Mapping using Three-layered Selective Audio Rays for Mobile Robots', The 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeon, Korea, pp. 2771-2777.
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This paper investigates sound source mapping in a real environment using a mobile robot. Our approach is based on audio ray tracing which integrates occupancy grids and sound source localization using a laser range finder and a microphone array. Previous audio ray tracing approaches rely on all observed rays and grids. As such observation errors caused by sound reflection, sound occlusion, wall occlusion, sounds at misdetected grids, etc. can significantly degrade the ability to locate sound sources in a map. A three-layered selective audio ray tracing mechanism is proposed in this work. The first layer conducts frame-based unreliable ray rejection (sensory rejection) considering sound reflection and wall occlusion. The second layer introduces triangulation and audio tracing to detect falsely detected sound sources, rejecting audio rays associated to these misdetected sounds sources (short-term rejection). A third layer is tasked with rejecting rays using the whole history (long-term rejection) to disambiguate sound occlusion. Experimental results under various situations are presented, which proves the effectiveness of our method.
Cui, Y.D., Poon, J.T., Matsubara, T., Valls Miro, J., Sugimoto, K. & Yamazaki, K. 2016, 'Environment-adaptive Interaction Primitives for Human-Robot Motor Skill Learning', IEEE/RAS International Conference on Humanoid Robots, IEEE, Cancun, Mexico, pp. 711-717.
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Falque, R., Vidal Calleja, T.A., Dissanayake, G. & Valls Miro, J. 2016, 'From the skin-depth equation to the inverse RFEC sensor model', 14th International Conference on Control, Automation, Robotics and Vision (ICARCV 2016), IEEE, Phuket, Thailand.
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Emery, B.M., Ghaffari Jadidi, M., Nakamura, K. & Valls Miro, J. 2016, 'An Audio-visual Solution to Sound Source Localization and Tracking with Applications to HRI', Wesite proceedings of the Australasian Conference on Robotics & Automation (ACRA), Australasian Conference on Robotics & Automation, ARAA, Brisbane, pp. 1-10.
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Robot audition is an emerging and growing branch in the robotic community and is necessary for a natural Human-Robot Interaction (HRI). In this paper, we propose a framework that integrates advances from Simultaneous Localization And Mapping (SLAM), bearing-only target tracking, and robot audition techniques into a unifed system for sound source identification, localization, and tracking. In indoors, acoustic observations are often highly noisy and corrupted due to reverberations, the robot ego-motion and background noise, and possible discontinuous nature of them. Therefore, in everyday interaction scenarios, the system requires accommodating for outliers, robust data association, and appropriate management of the landmarks, i.e. sound sources. We solve the robot self-localization and environment representation problems using an RGB-D SLAM algorithm, and sound source localization and tracking using recursive Bayesian estimation in the form of the extended Kalman Filter with unknown data associations and an unknown number of landmarks. The experimental results show that the proposed system performs well in the medium-sized cluttered indoor environment.
Sun, L., Vidal Calleja, T.A. & Valls Miro, J. 2015, 'Bayesian Fusion using Conditionally Independent Submaps for High Resolution 2.5D Mapping', Proceedings of 2015 IEEE International Conference on Robotics and Automation, 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Seattle, Washington, United States, pp. 3394-3400.
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— Typically 2.5D maps provide a compact and effi- cient representation of the environment. When sensor data is obtained from multiple sets of noisy measurements at differing resolutions, the problem of compounding this information together to provide an effective and efficient means of mapping is not trivial, particularly as the size of the environment increases. In this paper, we propose a general framework for integrating heterogeneous sensor data to obtain largescale 2.5D probabilistic maps. Gaussian Processes are used to generate a prior map that learns the spatial correlation between nearby points. Bayesian data fusion is then employed to update these prior maps with new measurements from distinct sensor modalities. In order to deal with large scale data, a novel submapping strategy is introduced to perform the fusion step efficiently in dealing with large covariance matrices. Submaps are first marginalised from the learned correlated prior and then updated based on the property of conditional independence. Most notably, the technique lends itself to generate accurate estimates at arbitrary resolutions and is able to handle varying noise from disparate sensor sources. The framework is applied to pipeline thickness mapping, with experimental results in fusing a high-resolution sensor and a low-resolution sensor showing the ability of the proposed technique to capture spatial correlations to come up with more accurate results when compared with a na¨ıve fusion approach.
Wijerathna, B.S., Kodagoda, S., Valls Miro, J. & Dissanayake, G. 2015, 'Iterative Coarse to Fine Approach for Interpretation of Defect Profiles Using MFL Measurements', Proceedings of the IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Auckland, New Zealand.
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Magnetic Flux Leakage (MFL) is a commonly used technology for non destructive evaluation of ferromagnetic materials. MFL in general is used to estimate isolated defect geometry. In this study, a coarse to fine approach is proposed to interpret MFL measurements for continuous defect profiling. The coarse solution is implemented using a Gaussian Processes (GP) model and the fine approach is implemented using an unconstrained non-linear optimiser. This framework was tested on a 100 year old 600mm diameter cast iron pipe line. Some pipe sections were extracted, grit blasted and profiled using a sub millimetre accurate 3 - D laser scanner. The coarse to fine predictions were compared with the laser measured ground truth with just 1.2 mm RMS error.
Poon, J.T., Valls Miro, J. & Black, R.A. 2015, 'A Passive Estimator of Functional Degradation in Power Mobility Device Users', Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robots, IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015), Singapore, pp. 997-1002.
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This paper documents the development of a passive technique for assessing a power mobility device user's driving proficiency during everyday driving activities outside formal assessment conditions by therapists. This is approached by first building a model by means of an Artificial Neural Network to infer longer-term destinations for discretized bouts of travel, and subsequently drawing cues indicative of decline in driving proficiency for the duration of point-to-point navigation rather than relying on instantaneously calculated metrics. This resultant quantity, which we refer to as `functional degradation', can then provide therapists with additional information concerning user health or serve as a leveraging parameter in combinatory shared-control mobility frameworks. Experiments conducted by able-bodied users subject to simulated noise scaled to varying degrees of functional degradation reveal a quantitative correlation between these longer-term proficiency metrics and the magnitude of degradation experienced; a promising outcome that sets the scene for a larger-scale clinical trial.
Su, D., Vidal Calleja, T.A. & Valls Miro, J. 2015, 'Simultaneous asynchronous microphone array calibration and sound source localisation', 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Hamburg, Germany, pp. 5561-5567.
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In this paper, an approach for sound source localisation and calibration of an asynchronous microphone array is proposed to be solved simultaneously. A graph-based Simultaneous Localisation and Mapping (SLAM) method is used for this purpose. Traditional sound source localisation using a microphone array has two main requirements. Firstly, geometrical information of microphone array is needed. Secondly, a multichannel analog-to-digital converter is required to obtain synchronous readings of the audio signal. Recent works aim at releasing these two requirements by estimating the time offset between each pair of microphones. However, it was assumed that the clock timing in each microphone sound card is exactly the same, which requires the clocks in the sound cards to be identically manufactured. A methodology is hereby proposed to calibrate an asynchronous microphone array using a graph-based optimisation method borrowed from the SLAM literature, effectively estimating the array geometry, time offset and clock difference/drift rate of each microphone together with the sound source locations. Simulation and experimental results are presented, which prove the effectiveness of the proposed methodology in achieving accurate estimates of the microphone array characteristics needed to be used on realistic settings with asynchronous sound devices.
Falque, R., Vidal Calleja, T.A. & Valls Miro, J. 2015, 'Kidnapped Laser-Scanner for Evaluation of RFEC Tool', 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Hamburg, Germany, pp. 313-318.
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An algorithm is proposed for matching data from different sensing modalities. The problem is formalised as a kidnapped robot problem, where Bayesian fusion is used to find the most likely location where both modalities agree. The key idea of our algorithm is to model the correlation between the two modalities as a likelihood used to update a location prior. Data, in this case, is represented as 2.5D thickness maps from a laser scanner and a Remote Field Eddy Current (RFEC) tool, used in non-destructive testing to assess the condition of infrastructures. The laser data is limited, while RFEC data is continuous. Given some prior in location, the aim is to find the 2.5D thickness map from the laser that corresponds to the RFEC data, which should be noted is highly noisy. Real data from CCTV inspections of water pipes are used to validate the proposed approach.
Shi, L., Sun, L., Vidal Calleja, T. & Miro, J.V. 2015, 'Kernel-Specific Gaussian Process for Predicting Pipe Wall Thickness Maps', Website Proceedings of the Australasian Conference on Robotics and Automation 2015, Australasian Conference on Robotics and Automation 2015, AARA, Canberra, pp. 1-8.
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Data organised in 2.5D such as elevation and thickness maps has been extensively studied in the fields of robotics and geostatistics. These maps are typically a probabilistic 2D grid that stores an estimated value (height or thickness) for each cell. Modelling the spatial dependencies and making inference on new grid locations is a common task that has been addressed using Gaussian random fields. However, inference faraway from the training areas results quite uncertain, therefore not informative enough for some applications. The objective of this research is to model the status of a pipeline based on limited and sparse local assessments, predicting the likely condition on pipes that have not been inspected. A customised kernel for Gaussian Processes (GP) is proposed to capture the spatial correlation of the pipe wall thickness data. An estimate of the likely condition of non-inspected pipes is achieved by concretising GP to a multivariate Gaussian distribution and generating realisations from the distribution. The performance of this approach is evaluated on various thickness maps from the same pipeline, where data have been obtained by measuring the actual remaining wall thickness. The output of this work aims to serve as the input of
Matsubara, T., Valls Miro, J., Tanaka, D., Poon, J.T. & Sugimoto, K. 2015, 'Sequential Intention Estimation of a Mobility Aid User for Intelligent Navigational Assistance', Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication, IEEE International Symposium on Robot and Human Interactive Communication, IEEE, Kobe, Japan, pp. 444-449.
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This paper proposes an intelligent mobility aid framework aimed at mitigating the impact of cognitive and/or physical user deficiencies by performing suitable mobility assistance with minimum interference. To this end, a user action model using Gaussian Process Regression (GPR) is proposed to encapsulate the probabilistic and nonlinear relationships among user action, state of the environment and user intention. Moreover, exploiting the analytical tractability of the predictive distribution allows a sequential Bayesian process for user intention estimation to take place. The proposed scheme is validated on data obtained in an indoor setting with an instrumented robotic wheelchair augmented with sensorial feedback from the environment and user commands as well as proprioceptive information from the actual vehicle, achieving accuracy in near real-time of 80%. The initial results are promising and indicating the suitability of the process to infer user driving behaviors within the context of ambulatory robots designed to provide assistance to users with mobility impairments while carrying out regular daily activities.
Su, D., Valls Miro, J. & Vidal Calleja, T. 2015, 'Real-time Sound Source Localisation for Target Tracking Applications using an Asynchronous Microphone Array', The 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015), IEEE, Auckland, New Zealand.
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Su, D., Valls Miro, J. & Vidal Calleja, T. 2015, 'Graph-SLAM based calibration of an embedded asynchronousmicrophone array for outdoor robotic target tracking', Assistive Robotics: Proceedings of the International Conference on CLAWAR 2015, The 2015 International Conference on Climbing and Walking Robots (CLAWAR 2015), World Scientific, Hangzhou, Zhejiang Province, China, pp. 641-648.
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This paper presents a strategy for sound source localisation using an embedded asynchronous microphone array for robotic target tracking application. Conventional microphone array technologies require a multi-channel A/D converter for inter-microphone synchronization making the technology relatively expensive. In our method, a synchronization free embedded asynchronous microphone array has released this requirement. The microphone array needs self calibration using graph-based SLAM method, which estimates starting time offset and clock difference/drift of each microphone channel using Gauss-Newton least square optimization. Once calibrated, the asynchronous microphone array can be used to find the sound source direction using various Direction Of Arrival (DOA) estimation algorithms just like a synchronized one. The proposed method is suitable for target tracking applications. Specifically, this method is used for tracking a person with an outdoor service robot: Garden Utility Transportation System. Comprehensive simulations and experimental results are presented to show the validity of the algorithm.
Su, D., Valls Miro, J. & Vidal Calleja, T. 2015, 'Modelling In-Pipe Acoustic Signal Propagation for Condition Assessment of Multi-Layer Water Pipelines', Proceedings of the 10th IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications (ICIEA 2015), IEEE, Auckland, New Zealand, pp. 545-550.
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A solution to the condition assessment of fluid-filled conduits based on the analysis of in-pipe acoustic signal propagation is presented in this paper. The sensor arrangement consists of an acoustic emitter from which a known sonic pulse is generated, and a collocated hydrophone receiver that records the arrival acoustic wave at a high sampling rate. The proposed method exploits the influence of the surrounding environment on the propagation of an acoustic wave to estimate the condition of the pipeline. Specifically, the propagation speed of an acoustic wave is influenced by the hoop stiffness of the surrounding materials, a fact that has been exploited in the analysis of boreholes in the literature. In this work, this finding is extended to validate the analytical expression derived to infer the condition of uniform, axis-symmetric lined waterworks, a first step to ultimately be able to predict the remaining active life (time- to-failure) of pipelines with arbitrary geometries through finite element analysis (FEA). An investigation of the various aspects of the proposed methodology with typical pipe material and structures is presented to appreciate the advantages of modelling acoustic waves behaviours in fluid-filled cylindrical cavities for condition assessment of water pipelines.
Ghaffari Jadidi, M., Valls Miro, J. & Dissanayake, G. 2015, 'Mutual Information-based Exploration on Continuous Occupancy Maps', IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg Germany.
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The problem of active perception with an autonomous robot is studied in this paper. It is proposed that the exploratory behavior of the robot be controlled using mutual information (MI) surfaces between the current map and a one-step look ahead measurements. MI surfaces highlight informative areas for exploration. A novel method for computing these surfaces is described. An approach that exploits structural dependencies of the environment and handles sparse sensor measurements to build a continuous model of the environment, that can then be used to generate MI surfaces is also proposed. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian Process and provide frontier boundaries for further exploration. The continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic termination criterion for a desired sensitivity. The results from publicly available datasets confirm an average improvement of the proposed methodology over comparable standard and state-of-the-art exploratory methods available in the literature by more than 20% and 13% in travel distance and map entropy reduction rate, respectively.
Poon, J. & Valls Miro, J. 2015, 'Learning by Demonstration for Co-Operative Navigation with Assistive Mobility Devices', Australasian Conference on Robotics and Automation, Canberra.
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This work proposes a learning by demonstration framework for intuitive navigational adaptation of human-robot interactive mobile systems. Co-navigation algorithms for mobile robots tend to be highly parameterised with variables that may be difficult to manually configure to an individual user's desired behaviours by non-technical personnel, e.g. carers and therapists overseing activities with would-be intelligent power mobility devices. The proposed framework automatically learns suitable joystick inputs for safe handling of the platform from a healthy user aware of a desired subset of behaviours (generic collision avoidance, wall-following and forward/reverse alignment manoeuvres) through performance of a small set of elementary training exercises, without the need and risk associated to trial-and-error variable tuning. The paper compares the semi-autonomous capability of the proposed learning scheme with the popular Vector Field Histogram local planner in a corridor navigation task, showing its ability to safely generalise to different environments despite the simplicity of the training demonstrations.
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R. & Andrade-Cetto, J. 2014, 'Exploration on Continuous Gaussian Process Frontier Maps', ICRA 2014 Proceeding, The 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, pp. 6077-6082.
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An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in this paper. The approach conveniently handles sparse sensor measurements to build a continuous model of the environment that exploits structural dependencies without the need to resort to a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps.
Vidal Calleja, T.A., Su, D., De Bruijn, F. & Valls Miro, J. 2014, 'Learning Spatial Correlations for Bayesian fusion in Pipe Thickness Mapping', 2014 IEEE International Conference on Robotics and Automation, 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), IEEE, Hong Kong, pp. 1-8.
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Skinner, B., Vidal Calleja, T., Valls Miro, J., de Bruijn, F. & Falque, R. 2014, '3D Point Cloud Upsampling for Accurate Reconstruction of Dense 2.5D Thickness Maps.', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/at..., Australasian Conference on Robotics and Automation, Melbourne University.
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This paper presents a novel robust processing methodology for computing 2.5D thickness maps from dense 3D collocated surfaces. The proposed pipeline is suitable to faithfully adjust data representation detailing as required, from preserving fine surface features to coarse interpretations. The foundations of the proposed technique exploit spatial point-based filtering, ray tracing techniques and the Robust Implicit Moving Least Squares (RIMLS) algorithm applied to dense 3D datasets, such as those acquired from laser scanners. The effectiveness of the proposed technique in overcoming traditional angular aliasing and corruption artifacts is validated with 3D ranging data acquired from internal and external surfaces of exhumed water pipes. It is shown that the resulting 2.5D maps can be more accurately and completely computed to higher resolutions, while significantly reducing the number of raytracing errors when compared with 2.5D thickness maps derived from our current approach.
Vidal Calleja, T.A., Valls Miro, J., Martin, F., Lingnau, D. & Russell, D. 2014, 'Automatic Detection and Verification of Pipeline Construction Featureswith Multi-modal data', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, Chicago, IL, USA, pp. 3116-3122.
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Assessment of the condition of underground pipelines is crucial to avoid breakages. Autonomous in-line inspection tools provided with Non-destructive Technology (NDT) sensors to assess large sections of the pipeline are commonly used for these purposes. An example of such sensors based on Eddy currents is the Remote Field Technology (RFT). A crucial step during in-line inspections is the detection of construction features, such as joints and elbows, to accurately locate and size specific defects within pipe sections. This step is often performed manually with the aid of visual data, which results in slow data processing. In this paper, we propose a generic framework to automate the detection and verification of these construction features using both NDT sensor data and visual images. Firstly, supervised learning is used to identify the construction features in the NDT sensor signals. Then, image processing is employed to verify the selection. Results are presented with data from a RFT tool, for which a specialised descriptor has been designed to characterise and classify its signal features. Furthermore, the construction feature is displayed in the image, once it is identified in the RFT data and detected in the visual data. A visual odometry algorithm has been implemented to locate the visual data with respect to the RFT data. About 800 meters of these multi-modal data are evaluated to test the validity of the proposed approach.
Ulapane, N., Alempijevic, A., Vidal-Calleja, T., Valls Miro, J., Rudd, J. & Roubal, M. 2014, 'Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling', Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on, pp. 1762-1767.
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Liu, D.K., Dissanayake, G., Valls Miro, J. & Waldron, K.J. 2014, 'Infrastructure robotics: Research challenges and opportunities', 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings, pp. 43-49.
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Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney.
Norouzi, M., Valls Miro, J., Dissanayake, G. & Vidal-Calleja, T. 2014, 'Path planning with stability uncertainty for articulated mobile vehicles in challenging environments', IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 1748-1753.
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This article proposes a probabilistic approach to account for robot stability uncertainty when planing motions over uneven terrains. A novel probabilistic stability criterion derived from the cumulative distribution of a tip-over metric is introduced that allows a safety constraint to be dynamically updated by available sensor data as it becomes available. The proposed safety constraint authorizes the planner to generates more conservative motion plans for areas with higher levels of uncertainty, while avoids unnecessary caution in well-known areas. The proposed systematic approach is particularly applicable to reconfigurable robots that can assume safer postures when required, although is equally valid for fixed-configuration platforms to choose safer paths to follow. The advantages of planning with the proposed probabilistic stability metric are demonstrated with data collected from an indoor rescue arena, as well as an outdoor rover testing facility.
Falque, R., Vidal-Calleja, T., Valls Miro, J., Lingnau, D.C. & Russel, D.E. 2014, 'Background Segmentation to Enhance Remote Field Eddy Current Signals', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/at..., Australasian Conference on Robotics and Automation, Melbourne University.
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Pipe condition assessment is critical to avoid breakages. Remote Field Eddy Current (RFEC) is a commonly used technology to assess the condition of pipes. The nature of this technology induces some particular noise into its measurements. In this paper, we develop a 3D simulation based on the Finite Element Analysis to study the properties of this noise. Moreover, we propose filtering process based on a modified version of graph-cuts segmentation method to remove the influence of this noise. Simulated data together with an experimental data-set obtained from a real RFEC inspection show the validity of the proposed approach.
Andonovski, B., Valls Miro, J., Poon, J.T. & Black, R. 2014, 'An automated mechanism to characterize wheelchair user performance', Proceedings of 2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, 5th International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE, Sau Paulo, Brazil, pp. 444-449.
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This paper proposes a mechanism to derive quantitative descriptions of wheelchair usage as a tool to aid Occupational Therapist with their performance assesment of mobility platform users. This is accomplished by analysing data computed from a standalone sensor package fitted on an wheelchair platform. This work builds upon previous propositions where parameters that could assist in the assessment were recommended to the authors by a qualified occupational therapist (OT). In the current scheme however the task-specific parameters that may provide the most relevant user information for the assessment are automatically revealed through a machine learning approach. Data mining techniques are used to reveal the most informative parameters, and results from three typical classifiers are presented based on learnings from manual labelling of the training data. Trials conducted by healthy volunteers gave classifications with an 81% success rate using a Random Forest classifier, a promising outcome that sets the scene for a potential clinical trial with a larger user pool.
Poon, J.T. & Valls Miro, J. 2014, 'A Multi-Modal Utility to Assist Powered Mobility Device Navigation Tasks', Social Robotics: 6th International Conference, Icsr 2014, Sydney, Nsw, Australia, October 27-29, 2014. Proceedings, 6th International Conference on Social Robotics, Springer International Publishing, Sydney, Australia, pp. 300-309.
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This paper presents the development of a shared control system for power mobility device users of varying capability in order to reduce carer oversight in navigation. Weighting of a user's joystick input against a short-tem trajectory prediction and obstacle avoidance algorithm is conducted by taking into consideration proximity to obstacles and smoothness of user driving, resulting in capable users rewarded greater levels of manual control for undertaking maneuvres that can be considered more challenging. An additional optional comparison with a Vector Field Histogram applied to leader-tracking provides further activities, such as completely autonomous following and a task for the user to follow a leading entity. Indoor tests carried out on university campus demonstrate the viability of this work, with future trials at a care home for the disabled intended to show the system functioning in one of its intended settings.
Patel, M., Valls Miro, J. & Dissanayake, D. 2015, 'A Probabilistic Approach to Learn Activities of Daily Living of a Mobility Aid Device User', Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation, IEEE, Hong Kong, pp. 969-974.
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The problem of inferring human behaviour is naturally complex: people interact with the environment and each other in many different ways, and dealing with the often incomplete and uncertain sensed data by which the actions are perceived only compounds the difficulty of the problem. In this paper, we propose a framework whereby these elaborate behaviours can be naturally simplified by decomposing them into smaller activities, whose temporal dependencies can be more efficiently represented via probabilistic hierarchical learning models. In this regard, patterns of a number of activities typically carried out by users of an ambulatory aid device have been identified with the aid of a Hierarchical Hidden Markov Model (HHMM) framework. By decomposing the complex behaviours into multiple layers of abstraction the approach is shown capable of modelling and learning these tightly coupled human-machine interactions. The inference accuracy of the proposed model is proven to compare favourably against more traditional discriminative models, as well as other compatible generative strategies to provide a complete picture that highlights the benefits of the proposed approach, and opens the door to more intelligent assistance with a robotic mobility aid.
Su, D. & Valls Miro, J. 2014, 'An ultrasonic/RF GP-based sensor model robotic solution for indoors/outdoors person tracking', Proceedings of the 2014 13th International Conference on Control, Automation, Robotics & Vision, 2014 13th International Conference on Control, Automation, Robotics & Vision (ICARCV 2014), IEEE, Singapore, Singapore, pp. 1662-1667.
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An non-linear Bayesian regression engine for robotic tracking based on an ultrasonic/RF sensor unit is presented in this paper. The proposed system is able to maintain systematic tracking of a leading human in indoor/outdoor settings with minimalistic instrumentation. Compared to popular camera based localization system the sonar array/RF based system has the advantage of being insensitive to background light intensity changes, a primary concern in outdoor environments. In contrast to single-plane laser range finder based tracking the proposed scheme is able to better adapt to small terrain variations, while at the same time being a significantly more affordable proposition for tracking with a robotic unit. A key novelty in this work is the utilisation of Gaussian Process Regression (GPR) to build a model for the sensor unit, which is shown to compare favourably against traditional linear triangulation approaches. The covariance function yield by the GPR sensor model also provides the additional benefit of outlier rejection. We present experimental results of indoors and outdoors tracking by mounting the sensor unit on a Garden Utility Transportation System (GUTS) robot and compare the proposed approach with linear triangulation which clearly show the inference engine capability to generalise relative localisation of human and a marked improvement in tracking accuracy and robustness.
Martin, F., Valls Miro, J. & Moreno, L. 2014, 'Towards Exploiting the Advantages of Colour in Scan Matching', Springer International Publishing, pp. 217-231.
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Colour plays an important role in the perception systems of the human beings. In robotics, the development of new sensors has made it possible to obtain colour information together with depth information about the environment. The exploitation of this type of information has become more and more important in numerous tasks. In our recent work, we have developed an evolutionary-based scan matching method. The aim of this work is to modify this method by the introduction of colour properties, taking the first steps in studying how to use colour to improve the scan matching. In particular, we have applied a colour transition detection method based on the delta E divergence between neighbours in a scan. Our algorithm has been tested in a real environment and significant conclusions have been reached.
Wijerathna, B.S., Vidal Calleja, T.A., Kodagoda, S., Zhang, Q. & Valls Miro, J. 2013, 'Multiple defect interpretation based on Gaussian processes for MFL technology', Proceedings of SPIE - The International Society for Optical Engineering vol 8694 - Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2013, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2013, SPIE, San Diego, USA, pp. 1-12.
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Magnetic Flux Leakage (MFL) technology has been used in non-destructive testing for more than three decades. There have been several publications in detecting and sizing defects on metal pipes using machine learning techniques. Most of these literature focus on isolated defects, which is far from the real scenario.
Norouzi, M., Valls Miro, J. & Dissanayake, G. 2013, 'A Statistical Approach for Uncertain Stability Analysis of Mobile Robots', IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Germany, pp. 191-196.
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Stability prediction is an important concern for mobile robots operating in rough environments. Having the capacity to predict areas of instability means pro-actively being able to plan safer traversable paths. The most influential tip-over stability measures are based on two criteria, the robot's center of mass (CM) and the supporting polygon (SP) defined by the convex area spanned between the ground contact-points. However, there is significant uncertainty associated with many parameters in the planning pipe-line: the actual robot kino-dynamic model, its localisation in the ground, and the terrain models, particularly in uneven terrain. This article proposes a statistical analysis of stability prediction to account for some of the uncertainties. This is accomplished using the force angle (FA) stability measure for a reconfigurable multi-tracked vehicle fitted with flippers, a manipulator arm and a sensor head. Probability density function (PDF) of contact-points, CM and the FA stability measure are numerically estimated, with simulation results performed on the open dynamics engine (ODE) simulator based on uncertain parameters. Two techniques are presented: a conventional Monte Carlo scheme, and a structured unscented transform (UT) which results in significant improvement in computational efficiency. Experimental results on maps obtained from a range camera fitted on the sensor head while the robot traverses over a ramp and a series of steps are presented that confirms the validity of the proposed probabilistic stability prediction method.
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R., Andrade-Cetto, J. & Dissanayake, G. 2013, 'Exploration using an Information-Based Reaction-Diffusion Process', Proceedings of the 2013 Australasian Conference on Robotics & Automation, Australasian Conference on Robotics and Automation, Australian Robtocis and Automation Association, Sydney, Australia, pp. 1-10.
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Exploration using an Information-Based Reaction-Diffusion Process
Norouzi, M., Valls Miro, J. & Dissanayake, G. 2013, 'Planning Stable and Efficient Paths for Articulated Mobile Robots On Challenging Terrains', Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australasian Robotics and Automation Association, Sydney, Australia, pp. 1-10.
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An analytical strategy to generate stable paths for a reconfigurable vehicle while also meeting additional navigational objectives is herein proposed. The work is motivated by robots traversing over challenging terrains during search and rescue operations, such as those equipped with manipulator arms and/or flippers. The proposed solution looks at minimizing the length of the traversed path and the energy expenditure in changing postures, yet also accounts for additional constraints in terms of sensor visibility (i.e arm configurations close to those orthogonal to the horizontal global plane which can afford a wider sensor view) and traction (i.e. flipper angles that provide the largest trackterrain interaction area). The validity of the proposed planning approach is evaluated with a multitracked robot fitted with flippers and a range camera at the end of a manipulator arm while navigating over two challenging 3D terrain data sets: one in a mock-up urban search and rescue arena (USAR), and a second one from a publicly available quasi-outdoor rover testing facility (UTIAS).
Valls Miro, J., Black, R., Andonovski, B. & Dissanayake, G. 2013, 'Development of a Novel Evidence-Based Automated Powered Mobility Device Competency Assessment', Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics, IEEE, Seattle, WA, USA, pp. 1-8.
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This paper describes the outcomes of a clinical study to assess the validity of a stand-alone sensor package and algorithms to aid the assessment by an occupational therapist (OT) whether a person has the capacity to safely and effectively operate a powered mobility device such as a wheelchair in their daily activities. The proposed solution consists of a suite of sensors capable of inferring navigational characteristics from the platform it is attached to (e.g. trajectories, map of surroundings, speeds, distance to doors, etc). Such information presents occupational therapists with the ability to augment their own observations and assessments with correlated, quantitative, evidence-based data acquired with the sensor array. Furthermore, OT reviews can take place at the therapist's discretion as the data from the trials is logged. Results from a clinical evaluation of the proposed approach, taking as reference the commonly-used Power-Mobility Indoor Driving Assessment (PIDA) assessment, were conducted at the premises of the Prince of Wales (PoW) Hospital in Sydney by four users, showing consistency with the OT scores, and setting the scene to a larger study with wider targeted participation.
Patel, M.N., Ek, C.H., Kyriazis, N., Argyros, A., Valls Miro, J. & Kragic, D. 2013, 'Language for Learning Complex Human-Object Interactions', 2013 IEEE International Conference on Robotics and Automation (ICRA), 2013 IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 4997-5002.
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In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user.
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R., Andrade-Cetto, J. & Dissanayake, G. 2013, 'Exploration in Information Distribution Maps', Robotics Science and Systems - Workshop on Robotic Exploration, Monitoring and Information Collection, Robotics Science and Systems, Technische Universitat Berlin, Berlin, Germany.
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In this paper, a novel solution for autonomous robotic exploration is proposed. The distribution of information in an unknown environment is modeled as an unsteady diffusion process, which can be an appropriate mathematical formulation and analogy for expanding, time-varying, and dynamic environments. This information distribution map is the solution of the diffusion process partial differential equation, and is regressed from sensor data as a Gaussian Process. Optimization of the process parameters leads to an optimal frontier map which describes regions of interest for further exploration. Since the presented approach considers a continuous model of the environment, it can be used to plan smooth exploration paths exploiting the structural dependencies of the environment whilst handling sparse sensors measurements. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps.
Valls Miro, J., Poon, J. & Huang, S. 2012, 'Low-cost visual tracking with an intelligent wheelchair for innovative assistive care', International Conference on Control, Automation, Robotics & Vision, ICARCV 2012, IEEE, Guangzhou, China, pp. 1540-1545.
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This paper presents the development of a lowcost vision-based robotic wheelchair system towards autonomous convoying. The non-holonomic follower vehicle obtains visual real-time pose data of a known coplanar target installed on the back of the leading vehicle. This allows the tracking vehicle to mimic the path of the preceding vehicle, while maintaining a safe distance behind it with the aid of a controller based on the robots kinematics constraints. A back-end visual filter is proposed in the planning strategy to overcome the noisy environmental information acquired from the camera as it tracks the vehicle in front. The effectiveness of the approach is evaluated in an indoor setting using data obtained from an instrumented wheelchair platform and a low-cost camera, and validated with observations from a laser range finder and derived (known) maps of the environment
Patel, M.N., Valls Miro, J. & Dissanayake, G. 2012, 'A Hierarchical Hidden Markov Model to support activities of daily living with an assistive robotic walker', A Hierarchical Hidden Markov Model for Inferring Activities of Daily Living with an Assistive Robotic Walker, IEEE RAS/EMBS International Conference on Biomedical and Biomechatronics, IEEE RAS/EMBS International Conference on Biomedical and Biomechatronics, Rome, Italy, pp. 1071-1076.
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This paper proposes a Hierarchical Hidden Markov Model (HHMM) framework as the most suitable tool to exploit the interactions between an intelligent mobility aid and their human operator. The framework presented is capable of learning a mixed array of the Activities of Daily Living (ADL) that the typical user of these supportive devices would normally engage in, both navigational and non-navigational in nature, and provide assistance as and when required. The main contribution of this paper is the demonstration of how this probabilistic tool capable of modelling behaviours at multiple levels of abstraction is a natural embodiment of machine intelligence to support user activities. Effectiveness of the proposed HHMM framework is evaluated with a number of healthy volunteers using a conventional rolling walker equipped with sensing and navigational aids whilst operating in a structured environment resembling a home. A comparison with more traditional discriminative models and mixed generativediscriminative models is also presented to provide a complete picture that highlights the benefits of the proposed approach
Norouzi, M., Valls Miro, J. & Dissanayake, G. 2012, 'Planning High-Visibility Stable Paths for Reconfigurable Robots on Uneven Terrain', IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Algarve, Portugal, pp. 2844-2849.
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This paper proposes a motion planning strategy for reconfigurable mobile robots in uneven terrain. Paths that guarantee stability while at the same time maximise the height of the sensor payload, thereby enhancing the capacity of the robot to explore the environment are obtained using a search algorithm based on A*. This is particularly applicable to operations such as search and rescue where observing the environment for locating victims is the major objective, although the proposed technique can be generalised to incorporate other potentially conflicting objectives such as minimising energy. The proposed planning strategy looks at exploiting the (possibly incomplete) environment information available to the robot and/or operator as it explores novel terrain. The effectiveness of the approach is evaluated using data obtained from a multitracked robot fitted with a manipulator arm and a range camera in a mock-up search and rescue arena.
Valencia, R., Valls Miro, J., Dissanayake, G. & Andrade-Cetto, J. 2012, 'Active Pose SLAM', IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Algarve, Portugal, pp. 1885-1891.
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We present an active exploration strategy that complements Pose SLAM and optimal navigation in Pose SLAM. The method evaluates the utility of exploratory and place revisiting sequences and chooses the one that minimizes overall map and path entropies. The technique considers trajectories of similar path length taking marginal pose uncertainties into account. An advantage of the proposed strategy with respect to competing approaches is that to evaluate information gain over the map, only a very coarse prior map estimate needs to be computed. Its coarseness is independent and does not jeopardize the Pose SLAM estimate. Moreover, a replanning scheme is devised to detect significant localization improvement during path execution. The approach is tested in simulations in a common publicly available dataset comparing favorably against frontier based exploration.
Patel, M.N., Valls Miro, J. & Dissanayake, G. 2011, 'Activity Recognition from the Interactions between an Assistive Robotic Walker and Human Users', 6th ACM/IEEE International Conference on Human-Robot Interaction, ACM/IEEE International Conference on Human-Robot Interaction, ACM, Lausanne, Switzerland, pp. 221-222.
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Abstract Detection of individuals intention from a sequence of actions is an open and complex problem. In this paper we present a smart walker as mobility aid which can interpret the users behaviour patterns to recognize their intentions and consequently act as an intelligent assistant. The result of the experiments performed in this paper demonstrates the potential of dynamic bayesian networks (DBN), in relation to their dynamic and unsupervised nature, for realistic human-robot interaction modelling.
Taha, T., Valls Miro, J. & Dissanayake, G. 2011, 'A POMDP Framework for Modelling Human Interaction with Assistive Robots', IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Shanghai, China, pp. 544-549.
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This paper presents a framework for modelling the interaction between a human operator and a robotic device, that enables the robot to collaborate with the human to jointly accomplish tasks. States of the system are captured in a model based on a partially observable Markov decision process (POMDP). States representing the human operator are motivated by behaviours from the psychology of the human action cycle. Hierarchical nature of these states allows the exploitation of data structures based on algebraic decision diagrams (ADD) to efficiently solve the resulting POMDP. The proposed framework is illustrated using two examples from assistive robotics; a robotic wheel chair and an intelligent walking device. Experimental results from trials conducted in an office environment with the wheelchair is used to demonstrate the proposed technique.
Valls Miro, J., Zhou, W. & Dissanayake, G. 2012, 'A Strategy for Efficient Observation Pruning in Multi-Objective 3D SLAM', 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, San Francisco, CA, USA, pp. 1640-1646.
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An efficient automatic solution to the featurebased simultaneous localisation and mapping (SLAM) of mobile robots operating in conditions where a number of competing objectives operate simultaneously is proposed. The formulation quantitatively measures the merit of incoming data with respect to multiple priorities, automatically adjusting the amount of observations to be used in the estimation process for the best possible combined outcome. The methodology enables a selection mechanism which can efficiently exploit the observations available to the robot to best fulfil the objectives of differing tasks throughout the course of a mission, e.g. localisation, mapping, exploration, feature distribution, searching for specific objects or victims, etc. The work is particularly motivated by navigation in three-dimensional terrains, and an example considering the objectives of robot localisation and map expansion in a search and rescue environment using an RGB-D camera is utilised for discussion and results.
Valls Miro, J., De Bruijn, F., Dissanayake, G., Boisard, O. & Ton, P. 2011, 'Robot-Assisted Inspection of Concrete Box Girders in Bridges', Austroads Bridge Conference 2011: Sustainable Bridges - The Thread of Society, Austroads Bridge Conference 2011, Austroads Ltd., Sydney, pp. 335-346.
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This paper describes a robotic solution to the structural health inspection of concrete box girders. A combination of sensors including a laser range finder, a depth (RGB-D) camera, an inertial measurement unit and a high-resolution camera are mounted on a tracked robot. The robot can be driven inside a box girder even when there are steps present, to collect high-resolution images of the concrete structure. Software that allows the information captured to be registered with a 3D geometric map of the inside of a box girder, and a visualization tool that can be used to evaluate location-annotated highly-detailed surface condition pictures by the human operator has been developed. The proposed remote evaluation technique makes it feasible to safely monitor areas over a sequence of inspections, leading to more effective maintenance procedures. The effectiveness of the system is illustrated using data gathered during a field trial where the robot was deployed within the Overpass at Huntleyâs Point, in the junction between Victoria Road and Burns Bay Road in Sydney. Functionality to automatically identify defects/regions of interest is also being investigated. It is anticipated that supplementing manual inspection tasks with robotic aids will mitigate the risk to manual entry into a confined space with a consequential saving in associated cost. This project is collaboration between the University of Technology Sydney (UTS) and the Roads & Traffic Authority (RTA) of NSW.
Valls Miro, J., Black, R., De Bruijn, F. & Dissanayake, G. 2011, 'Semi-Autonomous Competency Assessment of Powered Mobility Device Users', IEEE International Conference on Rehabilitation Robotics, IEEE International Conference on Rehabilitation Robotics, IEEE, Zurich, Switzerland, pp. 1-6.
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This paper describes a stand-alone sensor package and algorithms for aiding the assessment by an occupational therapist whether a person has the capacity to safely and effectively operate a powered mobility device such as a walking aid or a wheelchair. The sensor package employed consists of a laser range finder, an RGB camera and an inertial measurement unit that can be attached to any mobility device with minimal modifications. Algorithms for capturing the data received by the sensor package and for generating the map of the environment as well as the trajectory of the mobility device have been developed. Such information presents occupational therapists with the capability to provide a quantitative assessment of whether patients are ready to be safely deployed with mobile aids for their daily activities. Preliminary evaluation of the sensor package and associated algorithms based on experiments, conducted at the premises of the Prince of Wales Hospital in Sydney, are presented.
Patel, M.N., Valls Miro, J. & Dissanayake, G. 2010, 'Dynamic Bayesian Networks for Learning Interactions between Assistive Robotic Walker and Human Users', Proceedings of the 33rd Annual German Conference on Artificial Intelligence (KI 2010): Advances in Artificial Intelligence, Annual German Conference on Artificial Intelligence, Springer-Verlag Berlin Heidelberg, Germany, pp. 333-340.
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Detection of individuals intentions and actions from a stream of human behaviour is an open problem. Yet for robotic agents to be truly perceived as human-friendly entities they need to respond naturally to the physical interactions with the surrounding environment, most notably with the user. This paper proposes a generative probabilistic approach in the form of Dynamic Bayesian Networks (DBN) to seamlessly account for users attitudes. A model is presented which can learn to recognize a subset of possible actions by the user of a gait stability support power rollator walker, such as standing up, sitting down or assistive strolling, and adapt the behaviour of the device accordingly. The communication between the user and the device is implicit, without any explicit intention such as a keypad or voice.The end result is a decision making mechanism that best matches the users cognitive attitude towards a set of assistive tasks, effectively incorporating the evolving activity model of the user in the process. The proposed framework is evaluated in real-life condition.
Patel, M.N., Khushaba, R.N., Valls Miro, J. & Dissanayake, G. 2010, 'Probabilistic Models versus Discriminate Classifiers for Human Activity Recognition with an Instrumented Mobility-Assistance Aid', Proceedings of the Australasian Conference on Robotics and Automation 2010 (ACRA 2010), Proceedings of the Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Brisbane, Queensland, Australia, pp. 1-8.
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Detection of individuals' intentions and actions from a stream of human behaviour is an open and complex problem. There is however an intrinsic need to automatically recognise the activities performed by users of mobility assistive aids to better understand their behavioural patterns, with the ultimate objective of improving the utility of these devices. While discriminative algorithms such as Support Vector Machines (SVM) are well understood, generative probabilistic approaches to machine learning such as Dynamic Bayesian Networks (DBN) have only recently started gaining increasing interest within the robotics community. In this paper, a comprehensive evaluation of these techniques is carried out for human activity recognition in the context of their applicability to assistive robotics. Results show comparable recognition rates, offering valuable insights into the advantageous characteristics of DBN in relation to their dynamic and unsupervised nature for realistic human-robot interaction modelling.
Valls Miro, J., Dumonteil, G., Beck, C. & Dissanayake, G. 2010, 'A kyno-dynamic metric to plan stable paths over uneven terrain', Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 294-299.
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A generic methodology to plan increasingly stable paths for mobile platforms travelling over uneven terrain is proposed in this paper. This is accomplished by extending the Fast Marching level-set method of propagating interfaces in 3D lattices with an analytical kyno-dynamic metric which embodies robot stability in the given terrain. This is particularly relevant for reconfigurable platforms which significantly modify their mass distribution through posture adaptation, such as robots equipped with manipulator arms or varying traction arrangements. Results obtained from applying the proposed strategy in a mobile rescue robot operating on simulated and real terrain data illustrate the validity of the proposed strategy.
Valls Miro, J. & Dissanayake, G. 2010, 'Automatic Fine Motor Control Behaviours for Autonomous Mobile Agents Operating on Uneven Terrains', The 3rd International Symposium on Practical Cognitive Agents and Robots, Practical Cognitive Agents and Robots, ACM, Toronto, Canada, pp. 33-40.
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A novel mechanism able to produce increasingly stable paths for mobile robotic agents travelling over uneven terrain is proposed in this paper. In doing so, cognitive agents can focus on higher-level goal planning, with the increased confidence the resulting tasks will be automatically accomplished via safe and reliable paths within the lower-level skills of the platform. The strategy proposes the extension of the Fast Marching level-set method of propagating interfaces in 3D lattices with a metric to reduce robot body instability. This is particularly relevant for kinematically reconfigurable platforms which significantly modify their mass distribution through posture adaptation, such as humanoids or mobile robots equipped with manipulator arms or varying traction arrangements. Simulation results of an existing reconfigurable mobile rescue robot operating on real scenarios illustrate the validity of the proposed strategy.
Valls Miro, J., Osswald, V., Patel, M.N. & Dissanayake, G. 2009, 'Robotic Assistance with Attitude: a Mobility Agent for Motor Function Rehabilitation and Ambulation Support', IEEE 11th International Conference on Rehabilitation Robotics (ICORR 2009), IEEE International Conference on Rehabilitation Robotics, IEEE, Kyoto, Japan, pp. 529-534.
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This paper presents the design of an intelligent walking aid for the frail and elderly as well as for patients who are recovering from surgical procedures, in order to enhance safer mobility for these study populations. The device augments a conventional rolling walker aid with sensing and navigational abilities to safely travel through an environment following user's perceived intentions, unless collisions or instability is imminent. The agent, embodied as a partially observable Markov decision process (POMDP), critically relies on minimal user input to seamlessly recognise user's short-term intended behaviour, constantly updating this projection to allow for inconspicuous user-robot integration. This, in turn, shifts user's focus from fine motor-skilled control to coarse indications broadly intended to convey intention. Overall, the system can afford an increase in safety for the cognitive user through preventative care - reduced number of falls or collision with surrounding objects, minimising health-care expenses as well as increasing independent living for people with gait disorders. Successful simulation and experimental results demonstrate the validity of the proposed architecture for a practical robotic rollator design.
Beck, C., Valls Miro, J. & Dissanayake, G. 2009, 'Trajectory Optimisation for Increased Stability of Mobile Robots Operating in Uneven Terrains', Seventh IEEE International Conference on Control and Automation, IEEE International Conference on Control and Automation, IEEE, Christchurch, NZ, pp. 1913-1919.
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A mechanism capable of enhancing the safety of paths followed by mobile robots which significantly modify their mass distribution while operating in uneven terrains is presented. This is the case, for instance, of kinematically reconfigurable platforms or robots equipped with manipulator arms. For a given path, a trajectory optimiser that finds suitably safer paths in terms of tip-over prevention and equal force distribution over the supporting contact points is proposed. Other kinematic considerations such as operating within given nominal joint positions or low energy motions can also be exploited to improve system stability while being deployed in specific domains such as security, rescue, etc. Simulation results of the proposed optimised motion planner for an iRobot Explorer tracked vehicle are presented. They are also compared with a non-optimised planners to show the validity of the approach.
Taha, T., Valls Miro, J. & Dissanayake, G. 2008, 'POMDP-Based Long-Term User Intention Prediction for Wheelchair Navigation', Proceeding of 2008 IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Pasadena, USA, pp. 3920-3925.
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This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. Several navigational techniques have been successfully developed in the past to assist with specific behaviours such as "door passing" or "corridor following". These shared control strategies normally require the user to manually select the level of assistance required during use. Recent research has seen a move towards more intelligent systems that focus on forecasting users' intentions based on current and past actions. However, these predictions have been typically limited to locations immediately surrounding the wheelchair. The key contribution of the work presented here is the ability to predict the users' intended destination at a larger scale, that of a typical office arena. The systems relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently drive the user to his destination. The projection is constantly being updated, allowing for true user- platform integration. This shifts users' focus from fine motor- skilled control to coarse control broadly intended to convey intention. Successful simulation and experimental results on a real wheelchair robot demonstrate the validity of the approach.
Taha, T., Valls Miro, J. & Dissanayake, G. 2008, 'Intention driven assistive wheelchair navigation', Proc. of "Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics", a Workshop at ACM/IEEE Human Robot Interaction 2008 (HRI 2008), ACM/IEEE Human Robot Interaction, Technical Report 470, University of Hertfordshire, Amsterdam, the Netherlands, pp. 71-77.
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This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. The system has the ability to predict the users intended destination at a larger scale, that of a typical office or home arena. This system relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently aid in driving the user to the destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users focus from fine motor-skilled control to coarse guidance, broadly intended to convey intention. Successful simulation and experimental results on a real automated wheelchair platform demonstrate the validity of the approach.
Valls Miro, J. & Dissanayake, G. 2008, 'Robotic 3D Visual Mapping for Augmented Situational Awareness in Unstructured Environments', Proceedings of the EURON/IARP International Workshop on Robotics for Risky Interventions and Surveillance of the Environment (RISE 2008), EURON/IARP Robotics for Risky Interventions and Surveillance of the Environment, University of Jaume I, Technical Report "Colleccio e-Treballs d'Informatica i Technologia" Num. 4, Benicassim, Spain.
Valls Miro, J. 2008, 'Robot assisted urban search and rescue (USAR)', Emergency Management Conference: Rescue 08, Emergency Management Conference: Rescue 08, Emergency Services Foundation (ESF), Melbourne, Vic, Australia.
Zhou, W., Valls Miro, J. & Dissanayake, G. 2008, 'Information-Driven 6D SLAM Based on Ranging Vision', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2008, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Nice, France, pp. 2072-2077.
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This paper presents a novel solution for building three-dimensional dense maps in unknown and unstructured environment with reduced computational costs. This is achieved by giving the robot the dasiaintelligencepsila to select, out of the steadily collected data, the maximally informative observations to be used in the estimation of the robot location and its surroundings. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data is not heuristically segmented prior to be input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal and is capable of handling large data sets collected at realistic sampling rates. The strategy is generic to any 3D feature-based simultaneous localization and mapping (SLAM) algorithm in the information form, but in the work presented here it is closely coupled to a proposed novel appearance-based sensory package. It consists of a conventional camera and a range imager, which provide range, bearing and elevation inputs to visual salient features as commonly used by three-dimensional point-based SLAM, but it is also particularly well adapted for lightweight mobile platforms such as those commonly employed for Urban Search and Rescue (USAR), chosen here to demonstrate the excellences of the proposed strategy
Taha, T., Valls Miro, J. & Dissanayake, G. 2007, 'Wheelchair driver assistance and intention prediction using POMDPs', IEEE International Conference on Intelligent Networks, Sensor Networks and Information Processing (ISSNIP 2007), International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE Computer Society, Melbourne, Victoria, pp. 449-454.
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Electric wheelchairs give otherwise immobile people the freedom of movement, they significantly increase independence and dramatically increase quality of life. However the physical control systems of such wheelchair can be prohibitive for some users; for example, people with severe tremors. Several assisted wheelchair platforms have been developed in the past to assist such users. Algorithms that assist specific behaviors such as door - passing, follow - corridor, or avoid - obstacles have been successful. Research has seen a move towards systems that predict the users intentions, based on the users input. These predictions have been typically limited to locations immediately surrounding the wheelchair. This paper presents a new assisted wheelchair driving system with large scale intelligent intention recognition based on POMDPs (partially observable Markov decision processes). The systems acts as an intelligent agent/decision-maker, it relies on minimal user input; to predict the users intention and then autonomously drives the user to his destination. The prediction is constantly being updated as new user input is received allowing for true user/system integration. This shifts the users focus from fine motor-skilled control to coarse control intended to convey intention.
Zhou, W., Valls Miro, J. & Dissanayake, G. 2007, 'Information efficient 3D visual SLAM in unstructured domains', IEEE International Conference on Intelligent Networks, Sensor Networks and Information Processing (ISSNIP 2007), International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE, Melbourne, Victoria, pp. 323-328.
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This paper presents a strategy for increasing the efficiency of simultaneous localisation and mapping (SLAM) in unknown and unstructured environments using a vision-based sensory package. Traditional feature-based SLAM, using either the extended Kalman filter (EKF) or its dual, the extended information filter (EIF), leads to heavy computational costs while the environment expands and the number of features increases. In this paper we propose an algorithm to reduce computational cost for real-time systems by giving robots the 'intelligence' to select, out of the steadily collected data, the maximally informative observations to be used in the estimation process. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data is not heuristically segmented prior to be input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal.
Pedraza, L., Dissanayake, G., Valls Miro, J., Rodriguez-Losada, D. & Matia, F. 2007, 'BS-SLAM: shaping the world', Robotics: Science and Systems III(RSS 2007), Robotics: Systems and Science, MIT Press, Atlanta, GA, USA, pp. 1-8.
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This paper presents BS-SLAM, a simultaneous localization and mapping algorithm for use in unstructured environments that is effective regardless of whether features correspond to simple geometric primitives such as points and lines or not. The coordinates of the control points defining a set of B-splines are used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman filter based SLAM algorithm. The proposed method is the first known EKF-SLAM implementation capable of describing both straight and curve features in a parametric way. Appropriate observation equation that allows the exploitation of virtually all observations from a range sensor such as the ubiquitous laser range finder is developed. Efficient strategies for computing the relevant Jacobians, perform data association, initialization and expanding the map are presented. The effectiveness of the algorithms is demonstrated using experimental data.
Valls Miro, J., Taha, T., Wang, D., Dissanayake, G. & Liu, D. 2007, 'An efficient strategy for robot navigation in cluttered environments in the presence of dynamic obstacles', Proceedings of the 8th International Conference on Intelligent Technologies (InTech), International Conference on Intelligent Technologies, University of Technology, Sydney, Sydney, Australia, pp. 74-81.
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Valls Miro, J., Zhou, W. & Dissanayake, G. 2006, 'Towards Vision Based Navigation in Large Indoor Environments', Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Beijing, China, pp. 2096-2102.
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The main contribution of this paper is a novel stereo-based algorithm which serves as a tool to examine the viability of stereo vision solutions to the simultaneous localisation and mapping (SLAM) for large indoor environments. Using features extracted from the scale invariant feature transform (SIFT) and depth maps from a small vision system (SVS) stereo head, an extended Kalman filter (EKF) based SLAM algorithm, that allows the independent use of information relating to depth and bearing, is developed. By means of a map pruning strategy for managing the computational cost, it is demonstrated that statistically consistent location estimates can be generated for a small (6 m times 6 m) structured office environment, and in a robotics search and rescue arena of similar size. It is shown that in a larger office environment, the proposed algorithm generates location estimates which are topologically correct, but statistically inconsistent. A discussion on the possible reasons for the inconsistency is presented. The paper highlights that, despite recent advances, building accurate geometric maps of large environments with vision only sensing is still a challenging task
Ellekilde, L., Valls Miro, J. & Dissanayake, G. 2006, 'Fusing range and intensity images for generating dense models of three-dimensional environments', Proceedings of the 2006 IEEE International Conference on Man-Machine Systems (ICoMMS 2006), IEEE International Conference on Man-Machine Systems, IET, Langkawi, Malaysia, pp. 1-5.
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Taha, T., Valls Miro, J. & Dissanayake, G. 2006, 'Sampling based time efficient path planning algorithm for mobile platforms', Proceeding of the 2006 IEEE International Conference on Man-Machine Systems (ICoMMS 2006), IEEE International Conference on Man-Machine Systems, IET, Langkawi, Malaysia, pp. 1-6.
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Taha, T., Valls Miro, J. & Liu, D. 2007, 'An efficient path planner for large mobile platforms in cluttered enviornments', Proceedings of the 2006 IEEE International Conference on Robotics, Automation Mechatronics (RAM), IEEE Conference on Robotics, Automation and Mechatronics, IEEE, Bangkok, Thailand, pp. 225-230.
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This paper presents a one step smooth and efficient path planning algorithm for navigating a large robotic platform in known cluttered environments. The proposed strategy, based on the generation of a novel search space, relies on non-uniform density sampling of the free areas to direct the computational resources to troubled and difficult regions, such as narrow passages, leaving the larger open spaces sparsely populated. A smoothing penalty is also associated to the nodes to encourage the generation of gentle paths along the middle of the empty spaces. Collision detection is carried out off-line during the creation of the configuration space to speed up the actual search for the path, which is done on-line. Results prove that the proposed approach considerably reduces the search space in a meaningful and practical manner, improving the computational cost of generating a path optimised for fine and smooth motion
Dissanayake, G., Paxman, J.P., Valls Miro, J., Thane, O. & Thi, H. 2006, 'Robotics for urban search and rescue', Proceedings of the IEEE First International Conference on Industrial and Information Systems (ICIIS2006), IEEE International Conference on Industrial and Information Systems, IEEE, Peradeniya, Sri Lanka, pp. 294-298.
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This paper describes a team of robots that are designed for urban search and rescue applications. The team CASualty consists of four tele-operated robots and one autonomous robot. A brief description of the capabilities of the robot team is presented together with the details of capabilities of the autonomous robot HOMER. In particular, the software architecture, user interface, strategies used for mapping, exploration and the identification of human victims present in the environment are described. The team participated in an international competition on urban search and rescue (RoboCup Rescue) held in Bremen, Germany in June 2006 where HOMER was placed second in the autonomy challenge
Dissanayake, G., Paxman, J., Miro, J.V., Thane, O. & Thi, H.T. 2006, 'Robotics for urban search and rescue', 1st International Conference on Industrial and Information Systems, ICIIS 2006, pp. 294-298.
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This paper describes a team of robots that are designed for urban search and rescue applications. The team CASualty consists of four tele-operated robots and one autonomous robot. A brief description of the capabilities of the robot team is presented together with the details of capabilities of the autonomous robot HOMER. In particular, the software architecture, user interface, strategies used for mapping, exploration and the identification of human victims present in the environment are described. The team participated in an international competition on urban search and rescue (RoboCup Rescue) held in Bremen, Germany in June 2006 where HOMER was placed second in the autonomy challenge. ©2006 IEEE.
McLachlan, S., Arblaster, J.I., Liu, D., Valls Miro, J. & Chenoweth, L. 2005, 'A multi-stage shared control method for an intelligent mobility assistant', 2005 IEEE 9th International Conference On Rehabilitation Robotics, IEEE International Conference on Rehabilitation Robotics, IEEE, Chicago, USA, pp. 426-429.
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This paper presents a multi-stage shared control method (MSSC) which can be used to control the movement of a robotic mobility assistant designed to facilitate safe mobilization for people with unstable gait. The multi-stage control module consists of user intent, obstacle avoidance and fuzzy logic components. The user intent represents the persons commands. The obstacle avoidance component reads datarepresenting any obstacles in the vicinity of the assistant and uses the Vector Field Histogram (VFH) algorithm to select a suitable path to avoid any obstacles in the path of travel. The fuzzy logic component is responsible for merging the user intent and obstacle avoidance information such that the users request is satisfied to the highest extent possible. When an unsafe situation presents itself the users request(s) will be partially or wholly overridden so the assistant can return to a safe state. The system has been designed to be dynamically configurable so as to suit different users in terms of gait stability and strength, preferred speed of travel and level of control over the system. It has been tested both in a simulated environment and real-world operating conditions and has been shown to effectively avoid obstacles with minimal disruption to the user and their intent.
Valls Miro, J., Dissanayake, G. & Zhou, W. 2005, 'Vision-based SLAM using natural features in indoor environments', Proceedings of the 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing Conference., International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE Publications, Melbourne, Australia, pp. 151-156.
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This paper presents a practical approach to solve the simultaneous localization and mapping (SLAM) problem for autonomous mobile platforms by using natural visual landmarks obtained from an stereoscopic camera. It is an attempt to depart from traditional sensors such as laser rangefinders in order to gain the many benefits of nature-inspired information-rich 3D vision sensors. Whilst this makes the system fully observable in that the sensor provide enough information (range and bearing) to compute the full 2D estate of the observed landmarks from a single position, it is also true that depth information is difficult to rely on, particularly on measurements beyond a few meters (in fact the full 3D estate is observable, but here robot motion is constrained to 2D and only the 2D problem is considered). The work presented here is an attempt to overcome such a drawback by tackling the problem from a partially measurable SLAM perspective in that only landmark bearing from one of the cameras is employed in the fusion estimation. Range information estimates from the stereo pair is only used during map building in the landmark initialization phase in order to provide a reasonably accurate initial estimate. An additional benefit of the approach presented here lies in the data association aspect of SLAM. The availability of powerful feature extraction algorithms from the vision community, such as SIFT, permits a more flexible SLAM implementation separated from feature representation, extraction and matching, essentially carrying out matching with minimal recourse to geometry.

Journal articles

Norouzi, M., Valls Miro, J. & Dissanayake, G. 2016, 'Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains', Autonomous Robots, vol. 40, no. 2, pp. 361-381.
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© 2015, Springer Science+Business Media New York. A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility.
Kodikara, J., Valls Miro, J. & Melchers, R. 2016, 'Failure Prediction of Critical Cast Iron Pipes', Advances in Water Research, vol. 26, no. 3, pp. 6-11.
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In 2011, a consortium of Australian water utilities led by Sydney Water (SW) joined forces with WRF and UK Water Industry Research (UKWIR) to initiate a five-year research program, Advanced Condition Assessment and Pipe Failure Prediction Project (ACAPFP).
Valls Miro, J. & Shi, L. 2016, 'Aiming for the Holy Grail: Pipe Condition Assessment Along Critical Mains from Limited Inspections', Utility Magazine, vol. 10, pp. 90-92.
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The Advanced Condition Assessment and Pipe Failure Prediction Project is coming up with a novel condition assessment research concept: exploiting data-driven research to improve large critical water mains condition prediction, over extended sections of pipeline, from limited condition assessment inspection data.
Valiente, D., Ghaffari Jadidi, M., Valls Miró, J., Gil, A. & Reinoso, O. 2015, 'Information-based view initialization in visual SLAM with a single omnidirectional camera', Robotics and Autonomous Systems, vol. 72, pp. 93-104.
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© 2015 Elsevier B.V. All rights reserved. This paper presents a novel mechanism to initiate new views within the map building process for an EKF-based visual SLAM (Simultaneous Localization and Mapping) approach using omnidirectional images. In presence of non-linearities, the EKF is very likely to compromise the final estimation. Particularly, the omnidirectional observation model induces non-linear errors, thus it becomes a potential source of uncertainty. To deal with this issue we propose a novel mechanism for view initialization which accounts for information gain and losses more efficiently. The main outcome of this contribution is the reduction of the map uncertainty and thus the higher consistency of the final estimation. Its basis relies on a Gaussian Process to infer an information distribution model from sensor data. This model represents feature points existence probabilities and their information content analysis leads to the proposed view initialization scheme. To demonstrate the suitability and effectiveness of the approach we present a series of real data experiments conducted with a robot equipped with a camera sensor and map model solely based on omnidirectional views. The results reveal a beneficial reduction on the uncertainty but also on the error in the pose and the map estimate.
Martín, F., Miró, J.V. & Moreno, L. 2015, 'RGB-D DE-based Scan Matching: Exploiting Colour Properties in Registration', Journal of Intelligent & Robotic Systems, vol. 80, no. 1, pp. 71-85.
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Khushaba, R.N., Takruri, M., Valls Miro, J. & Kodagoda, S. 2014, 'Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.', Neural networks : the official journal of the International Neural Network Society, vol. 55, pp. 42-58.
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Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with 8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available a...
Patel, M., Valls Miro, J., Kragic, D., Ek, C.H. & Dissanayake, G. 2014, 'Learning object, grasping and manipulation activities using hierarchical HMMs', Autonomous Robots, vol. 37, no. 3, pp. 317-331.
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This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques. © 2014 Springer Science+Business Media New York.
Norouzi, M., De Bruijn, F. & Valls Miro, J. 2012, 'Planning Stable Paths for Urban Search and Rescue Robots', Lecture Notes in Computer Science, vol. 7416, pp. 90-101.
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Rescue robots are platforms designed to operate in challenging and uneven surfaces. These robots are often equipped with manipulator arms and varying traction arrangements. As such, it is possible to reconfigure the kinematic of robot in order to reduce potential instabilities, such as those leading to vehicle tip-over. This paper proposes a methodology to plan feasible paths through uneven topographies by planning stable paths that account for the safe interaction between vehicle and terrain. The proposed technique, based on a gradient stability criterion, is validated with two of the best known path search strategies in 3D lattices, i.e. the A* and the Rapidly-Exploring Random Trees. Using real terrain data, simulation results obtained with the model of a real rescue robot demonstrate significant improvements in terms of paths that are able to automatically avoid regions of potential instabilities, to concentrate on those where the freedom of exploiting posture adaptation permits generation of optimally safe paths.
Pedraza, L., Rodriguez-Losada, D., Matia, F., Dissanayake, G. & Valls Miro, J. 2009, 'Extending the Limits of Feature-Based SLAM With B-Splines', IEEE Transactions On Robotics, vol. 25, no. 2, pp. 353-366.
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This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments.
Valls Miro, J., Taha, T., Wang, D. & Dissanayake, G. 2008, 'An Adaptive Manoeuvring Strategy for Mobile Robots in Cluttered Dynamic Environments', International Journal of Automation and Control (IJAAC), vol. 2, no. 2/3, pp. 178-194.
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A novel method which combines an optimised global path planner with a real-time sensor-based collision avoidance mechanism to accommodate for dynamic changes in the environment (e.g., people) is presented. The basic concept is to generate a continually changing parameterised family of virtual force fields for the robot based on characteristics such as location, travelling speed and dimension of the objects in the vicinity, static and dynamic. The interactions among the repulsive forces associated with the various obstacles provide a natural way for local collision avoidance in a partially known cluttered environment. This is harnessed by locally modifying the planned behaviour of the moving platform in real-time, whilst preserving the optimised nature of the global path. Furthermore, path traversability is continually monitored by the global planner to trigger a complete path re-planning from the current location in case of major changes, most notably when the path is completely blocked by obstacles.
Zhou, W., Valls Miro, J. & Dissanayake, G. 2008, 'Information-Efficient 3-D Visual SLAM for Unstructured Domains', IEEE Transactions On Robotics, vol. 24, no. 5, pp. 1078-1087.
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This paper presents a novel vision-based sensory package and an information-efficient simultaneous localization and mapping (SLAM) algorithm. Together, we offer a solution for building 3-D dense map in an unknown and unstructured environment with minimal
Ellekilde, L., Huang, S., Valls Miro, J. & Dissanayake, G. 2007, 'Dense 3D Map Construction for Indoor Search and Rescue', Journal of Field Robotics, vol. 24, no. 1/2, pp. 71-89.
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The main contribution of this paper is a new simultaneous localization and mapping (SLAM) algorithm for building dense three-dimensional maps using information acquired from a range imager and a conventional camera, for robotic search and rescue in unstr
Valls Miro, J. & White, A.S. 2002, 'Modelling an industrial manipulator a case study', Simulation Practice and Theory, vol. 9, no. 6-8, pp. 293-319.
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A practical application of the modelling and validation of an open-chain industrial manipulator is presented in this paper. Both mechanical and electrical equations of motion were used to provide a complete model description. A model was obtained to enable an optimal path-planning controller to be designed. The paper describes how the equations of motion were derived and how the key parameters were obtained. The manipulator was simulated with TELEGRIP software. A validation procedure is illustrated and its' limitations exposed. The overall motion was found to give an agreement with the model predictions to within 86% for the smallest link and better than 96% for the major joints
Valls Miro, J. & White, A.S. 2002, 'Quasi-optimal trajectory planning and control of a CRS A251 industrial robot', Proceedings Of The Institution Of Mechanical Engineers Part I-Journal Of Systems And Control Engineering, vol. 216, no. 4, pp. 343-356.
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A near-optimal solution to the path-unconstrained time-optimal trajectory planning problem is described in this paper. While traditional trajectory planning strategies are entirely based on kinematic considerations, manipulator dynamics are usually neglected altogether. The strategy presented in this work has two distinguishing features. Firstly, the trajectory planning problem is reformulated as an optimal control problem, which is in turn solved using Pontryagin's maximum/minimum principle. This approach merges the traditional division of trajectory planning followed by trajectory tracking into one process. Secondly, the feedback form compensates for the dynamic approximation errors derived from linearization and the fundamental parameter uncertainty of the dynamic equations of motion. This approach can cope with flexible robots as well as rigid links. The terminal phase of the motion is controlled by a feedforward controller to reduce chatter vibrations. Results from simulations and an on-line implementation to a general-purpose open-chain industrial manipulator, the CRS A251, confirm the validity of the approach and show that maximizing the capabilities of the device can lead to an overall improvement in the manipulator time response of up to 24 per cent, while retaining an acceptable overshoot and steady state error regime.

Other

Shi, L. & Valls Miro, 'Utility Magazine'.
http://www.utilitymagazine.com.au/