To, W., paul, G. & liu, D. 2018, 'A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots', Robotics and Computer Integrated Manufacturing, vol. 49, pp. 13-23.View/Download from: UTS OPUS or Publisher's site
This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the lasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.
hassan, M., liu, D. & Paul, G. 2017, 'Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements', Journal of Intelligent and Robotic Systems.View/Download from: Publisher's site
Multiple autonomous industrial robots can be of great use in manufacturing applications, particularly if the environment is unstructured and custom manufacturing is required. Autonomous robots that are equipped with manipulators can collaborate to carry out manufacturing tasks such as surface preparation by means of grit-blasting, surface coating or spray painting, all of which require complete surface coverage. However, as part of the collaboration process, appropriate base placements relative to the environment and the target object need to be determined by the robots. The problem of finding appropriate base placements is further complicated when the object under consideration is large and has a complex geometric shape, and thus the robots need to operate from a number of base placements in order to obtain complete coverage of the entire object. To address this problem, an approach for Optimization of Multiple Base Placements (OMBP) for each robot is proposed in this paper. The approach aims to optimize base placements for multi-robot collaboration by taking into account task-specific objectives such as makespan, fair workload division amongst the robots, and coverage percentage; and manipulator-related objectives such as torque and manipulability measure. In addition, the constraint of robots maintaining an appropriate distance between each other and relative to the environment is taken into account. Simulated and real-world experiments are carried out to demonstrate the effectiveness of the approach and to verify that the simulated results are accurate and reliable.
Quin, P., Paul, G. & Liu, D. 2017, 'Experimental Evaluation of Nearest Neighbour Exploration Approach in Field Environments', IEEE Transactions on Automation Science and Engineering.View/Download from: Publisher's site
Inspecting surface conditions in 3-D environments such as steel bridges is a complex, time-consuming, and often hazardous undertaking that is an essential part of tasks such as bridge maintenance. Developing an autonomous exploration strategy for a mobile climbing robot would allow for such tasks to be completed more quickly and more safely than is possible with human inspectors. The exploration strategy tested in this paper, called the nearest neighbors exploration approach (NNEA), aims to reduce the overall exploration time by reducing the number of sensor position evaluations that need to be performed. NNEA achieves this by first considering at each time step only a small set of poses near to the current robot as candidates for the next best view. This approach is compared with another exploration strategy for similar robots performing the same task. The improvements between the new and previous strategy are demonstrated through trials on a test rig, and also in field trials on a ferromagnetic bridge structure.
To, W., Paul, G. & Liu, D. 2016, 'An approach for identifying classifiable regions of an image captured by autonomous robots in structural environments', Robotics and Computer Integrated Manufacturing, vol. 37, pp. 90-102.View/Download from: UTS OPUS or Publisher's site
When an autonomous robot is deployed in a structural environment to visually inspect surfaces, the capture conditions of images (e.g. camera's viewing distance and angle to surfaces) may vary due to un-ideal robot poses selected to position the camera in a collision-free manner. Given that surface inspection is conducted by using a classifier trained with surface samples captured with limited changes to the viewing distance and angle, the inspection performance can be affected if the capture conditions are changed. This paper presents an approach to calculate a value that represents the likelihood of a pixel being classifiable by a classifier trained with a limited dataset. The likelihood value is calculated for each pixel in an image to form a likelihood map that can be used to identify classifiable regions of the image. The information necessary for calculating the likelihood values is obtained by collecting additional depth data that maps to each pixel in an image (collectively referred to as a RGB-D image). Experiments to test the approach are conducted in a laboratory environment using a RGB-D sensor package mounted onto the end-effector of a robot manipulator. A naive Bayes classifier trained with texture features extracted from Gray Level Co-occurrence Matrices is used to demonstrate the effect of image capture conditions on surface classification accuracy. Experimental results show that the classifiable regions identified using a likelihood map are up to 99.0% accurate, and the identified region has up to 19.9% higher classification accuracy when compared against the overall accuracy of the same image.
The article discusses the maintenance of the Sydney Harbour Bridge, an old bridge located in Sydney, New South Wales. It highlights the use of robotic technology involving the abrasive blast-cleaning of the old paint back to bare metal, assisting the maintenance workers. It also emphasizes the role Roads and Maritime Services (RMS), an Australian government department in building, maintaining and delivering transport infrastructure and services in the area.
To, A.W., Paul, G. & Liu, D. 2014, 'Surface-type classification using RGB-D', IEEE Transactions on Automation Science and Engineering, vol. 11, no. 2, pp. 359-366.View/Download from: UTS OPUS or Publisher's site
This paper proposes an approach to improve surface-type classification of images containing inconsistently illuminated surfaces. When a mobile inspection robot is visually inspecting surface-types in a dark environment and a directional light source is used to illuminate the surfaces, the images captured may exhibit illumination variance that can be caused by the orientation and distance of the light source relative to the surfaces. In order to accurately classify the surface-types in these images, either the training image dataset needs to completely incorporate the illumination variance or a way to extract color features that can provide high classification accuracy needs to be identified. In this paper diffused reflectance values are extracted as new color features to classifying surface-types. In this approach, Red, Green, Blue-Depth (RGB-D) data is collected from the environment, and a reflectance model is used to calculate a diffused reflectance value for a pixel in each Red, Green, Blue (RGB) color channel. The diffused reflectance values can be used to train a multiclass support vector machine classifier to classify surface-types. Experiments are conducted in a mock bridge maintenance environment using a portable RGB-Depth sensor package with an attached light source to collect surface-type data. The performance of a classifier trained with diffused reflectance values is compared against classifiers trained with other color features including RGB and L*a*b* color spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and L*a*b* features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in...
Paul, G., Kwok, N.M. & Liu, D. 2013, 'A novel surface segmentation approach for robotic manipulator-based maintenance operation planning', Automation In Construction, vol. 29, pp. 136-147.View/Download from: UTS OPUS or Publisher's site
This paper presents a novel approach to segmenting a three-dimensional surface map by considering the task requirements and the movements of an industrial robot manipulator. Maintenance operations, such as abrasive blasting, that are performed by a field robot manipulator can be made more efficient by exploiting surface segmentation. The approach in this paper utilises an aggregate of multiple connectivity graphs, with graph edges defined by task constraints, and graph vertices that correspond to small, maintenance-specific target surfaces, known as Scale-Like Discs (SLDs). The task constraints for maintenance operations are based on the characteristics of neighbouring SLDs. The combined connectivity graphs are analysed to find clusters of vertices, thus segmenting the surface map into groups of related SLDs. Experiments conducted in three typical bridge maintenance environments have shown that the approach can reduce garnet usage by 10%â40% and reduce the manipulator joint movements by up to 35%.
Paul, G., Webb, S.S., Liu, D. & Dissanayake, G. 2011, 'Autonomous Robot Manipulator-Based Exploration And Mapping System For Bridge Maintenance', Robotics And Autonomous Systems, vol. 59, no. 7-8, pp. 543-554.View/Download from: UTS OPUS or Publisher's site
This paper presents a system for Autonomous eXploration to Build A Map (AXBAM) of an unknown, 3D complex steel bridge structure using a 6 degree-of-freedom anthropomorphic robot manipulator instrumented with a laser range scanner. The proposed algorithm considers the trade-off between the predicted environment information gain available from a sensing viewpoint and the manipulator joint angle changes required to position a sensor at that viewpoint, and then obtains collision-free paths through safe, previously explored regions. Information gathered from multiple viewpoints is fused to achieve a detailed 3D map. Experimental results show that the AXBAM system explores and builds quality maps of complex unknown regions in a consistent and timely manner.
Paul, G., Liu, D., Kirchner, N.G. & Dissanayake, G. 2009, 'An Effective Exploration Approach to Simultaneous Mapping and Surface Material-Type Identification of Complex Three-Dimensional Environments', Journal of Field Robotics, vol. 26, no. 11-12, pp. 915-933.View/Download from: UTS OPUS or Publisher's site
This paper presents an integrated exploration approach for geometric mapping and surface material-type identification of complex three-dimensional (3D) environments using a six-degree-of-freedom industrial robot manipulator. Maps of the surface geometry with the surface material type identified are required for an autonomous robotic system to perform operations in steel bridge maintenance. The proposed approach utilizes information theory to enable multiobjective exploration while new 3D geometric and surface-type data are fused via probabilistic updates. It is verified that the integrated approach enables the robotic system to perform exploration and surface inspection in real-world environments.
To, A.W., Paul, G., Kwok, N. & Liu, D. 2009, 'An efficient trajectory planning approach for autonomous robots in complex bridge environments', International Journal of Computer Aided Engineeri..., vol. 1, no. 2, pp. 185-208.View/Download from: UTS OPUS
This paper presents an efficient trajectory planning approach for a 6DOF robotic manipulator conducting grit-blasting in complex bridge structural environments. The proposed approach extends upon robotic grit-blasting planning and incorporates joint movement minimisation in addition to path length minimisation. A genetic algorithm is implemented to optimise initial path plans based on a heuristic pattern for the coverage of surface areas to be blasted. A customised gradient based method is applied for the generation of collision-free joint configurations for grit-blasting based on the identified path plan. A grit-blasting coverage model is developed for discrete non-planar 3D coverage determination to verify the performance of the plan. Extensive simulation and experimental results are also presented in this paper.
Kirchner, N.G., Paul, G. & Liu, D. 2006, 'Bridge Maintenance Robotic Arm: Mechanical Technique to reduce the nozzle Force of a Sandblasting Rig', Journal of Wuhan University of Technology, vol. 28, no. 164, pp. 12-18.
Paul, G., Liu, D. & Kirchner, N.G. 2007, 'An algorithm for surface growing from laser scan generated point clouts' in Tarn, T.J., Chen, S.B. & Zhou, C. (eds), Robotic Welding, Intelligence and Automation, Springer, Heidelberg, pp. 481-491.View/Download from: UTS OPUS or Publisher's site
n robot applications requiring interaction with a partially/unknown environment, mapping is of paramount importance. This paper presents an effective surface growing algorithm for map building based on laser scan generated point clouds. The algorithm directly converts a point cloud into a surface and normals form which sees a significant reduction in data size and is in a desirable format for planning the interaction with surfaces. It can be used in applications such as robotic cleaning, painting and welding.
Han, H., Paul, G. & Matsubara, T. 2017, 'Model-Based Reinforcement Learning Approach for Deformable Linear Object Manipulation', IEEE Conference on Automation Science and Engineering, Xi'an, China.View/Download from: UTS OPUS
Deformable Linear Object (DLO) manipulation has wide application in industry and in daily life. Conventionally, it is difficult for a robot to manipulate a DLO to achieve the target configuration due to the absence of the universal model that specifies the DLO regardless of the material and environment. Since the state variable of a DLO can be very high dimensional, identifying such a model may require a huge number of samples. Thus, model-based planning of DLO manipulation would be impractical and unreasonable. In this paper, we explore another approach based on reinforcement learning. To this end, our approach is to apply a sample-efficient model-based reinforcement learning method, so-called PILCO, to resolve the high dimensional planning problem of DLO manipulation with a reasonable number of samples. To investigate the effectiveness of our approach, we developed an experimental setup with a dual-arm industrial robot and multiple sensors. Then, we conducted experiments to show that our approach is efficient by performing a DLO manipulation task.
Shakor, P., Renneberg, J., Nejadi, S. & Paul, G. 2017, 'Optimisation of Different Concrete Mix Designs for 3D Printing by Utilising 6DOF Industrial Robot', 34th International Symposium on Automation and Robotics in Construction, Taipei, Taiwan.View/Download from: UTS OPUS
Additive Manufacturing (AM) technologies are becoming increasingly viable for commercial and research implementation into various applications. AM refers to the process of forming structures layer upon layer and finds application in prototyping and manufacturing for building construction. It has recently begun to be considered as a viable and attractive alternative in certain circumstances in the construction industry. This paper focuses on the utilisation of different concrete mixtures paired with extrusion techniques facilitated by a six Degree of Freedom (DOF) industrial robot. Using methods of Damp Least Squares (DLS) in conjunction with Resolved Motion Rate Control (RMRC), it is possible to plan stable transitions between several waypoints representing the various print cross-sections. Calculated paths are projected via 'spline' interpolation into the manipulator controlled by custom software. This article demonstrates the properties of different concrete mixture designs, showing their performance when used as a filament in 3D Printing and representing a comparison of the results that were found. In this study, the prepared materials consist of ordinary Portland cement, fine sand between (425~150) micron, coarse aggregate ranges (3) mm and chemical admixtures which have been used to accelerate setting times and reduce water content. Numerous tests were performed to check the buildability, flowability, extrudability and moldability of the concrete mixtures. The horizontal test was used to determine the flowability and consistency, while the vertical and squeeze-flow tests were used to determine the buildability of the layers. The extrudability and moldability of the concrete mixtures were controlled by the robot and associated extruder speeds.
Paul, G., Mao, S., Liu, L. & Xiong, R. 2015, 'Mapping Repetitive Structural Tunnel Environments for a Biologically Inspired Climbing Robot', Assistive Robots: Proceedings of the 18th International Conference on CLAWAR 2015, International Conference on Climbing and Walking Robots, World Scientific, Hangzhou, China, pp. 325-333.View/Download from: UTS OPUS or Publisher's site
This paper presents an approach to using noisy and incomplete depth-camera datasets to detect
reliable surface features for use in map construction for a caterpillar-inspired climbing robot.
The approach uses a combination of plane extraction, clustering and template matching techniques to
infer from the restricted dataset a usable map. This approach has been tested in both laboratory
and real-world steel bridge tunnel datasets generated by a climbing robot, with the results showing
that the generated maps are accurate enough for use in localisation and step trajectory planning.
Hassan, M., Liu, D.L. & Paul, G.P. 2016, 'Modeling and Stochastic Optimization of Complete Coverage under Uncertainties in Multi-Robot Base Placements', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (Institute of Electrical and Electronics Engineers), Daejeon, Korea, pp. 2978-2984.View/Download from: UTS OPUS or Publisher's site
Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a real-world object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.
Paul, G., Liu, L. & Liu, D. 2016, 'A Novel Approach to Steel Rivet Detection in Poorly Illuminated Steel Structural Environments', Control, Automation, Robotics and Vision (ICARCV), 2016 14th International Conference on, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.View/Download from: UTS OPUS or Publisher's site
It is becoming increasingly achievable for steel
bridge structures, which are normally both inaccessible and
hazardous for humans, to be inspected and maintained by
autonomous robots. Steel bridges have been traditionally constructed
by securing plate members together with rivets. However,
rivets present a challenge for robots both in terms of cleaning and
surface traversal. This paper presents a novel approach to RGBD
image and point cloud analysis that enables rivets to be rapidly
and robustly located using low cost, non-contact sensing devices
that can be easily affixed to a robot. The approach performs
classification based on: (a) high-intensity blobs in color images,
(b) the non-linear perturbations in depth images, and (c) surface
normal clusters in 3D point clouds. The predicted rivet locations
from the three classifiers are combined using a probabilistic
occupancy mapping technique. Experiments are conducted in
several different lab and real-world steel bridge environments,
where there is no external lighting infrastructure, and the sensors
are attached to a mobile platform, i.e. a climbing inspection robot.
The location of rivets within 2m of the robot can be robustly
located within 10mm of their correct location. The state of voxels
can be predicted with above 95% accuracy, in approximately 1
second per frame.
Yang, C., Paul, G., Ward, P. & Liu, D. 2016, 'A Path Planning Approach Via Task-Objective Pose Selection with Application to an Inchworm-Inspired Climbing Robot', IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Banff, Canada, pp. 401-406.View/Download from: UTS OPUS or Publisher's site
This paper presents a stepping path planning
approach for a climbing robot inspired kinematically from
an inchworm caterpillar's looping locomotion. This approach
generates an optimised multi-step path to traverse through
space and to land a specific footpad onto a selected point on
a surface with a specific footpad orientation. The candidate
landing joint configuration for each step is generated by a pose
selection process, using an optimisation technique with task-
objective functions based on the constraints of the robot. Then
another technique is used to obtain a new set of poses satisfying
strict constraints of the landing motion. The set of candidate
landing poses is used to compute the subsequent steps. A valid
motion trajectory, which avoids all obstacles, can be generated
by a point-to-point planner for each of the landing poses from
the current pose. This single step planning technique is then
expanded to multi-step path planning by building a search
tree, where a combination of steps is evaluated and optimised
by a cost function, which includes objectives related to robot
movement. This approach is implemented and validated on
the climbing robot in real-world steel bridge environments.
The planner successfully finds multi-step paths in these field
trials enabling the robot to traverse through several complex
structures inside the bridge steel box girders.
Quin, P.D., Paul, G., Alempijevic, A. & Liu, D. 2016, 'Exploring in 3D with a Climbing Robot: Selecting the Next Best Base Position on Arbitrarily-Oriented Surfaces', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 5770-5775.View/Download from: UTS OPUS or Publisher's site
This paper presents an approach for selecting the next best base position for a climbing robot so as to observe the highest information gain about the environment. The robot is capable of adhering to and moving along and transitioning to surfaces with arbitrary orientations. This approach samples known surfaces, and takes into account the robot kinematics, to generate a graph of valid attachment points from which the robot can either move to other positions or make observations of the environment. The information value of nodes in this graph are estimated and a variant of A* is used to traverse the graph and discover the most worthwhile node that is reachable by the robot. This approach is demonstrated in simulation and shown to allow a 7 degree-of-freedom inchworm-inspired climbing robot to move to positions in the environment from which new information can be gathered about the environment.
Paul, G., Quin, P., To, A. & Liu, D. 2015, 'A Sliding Window Approach to Exploration for 3D Map Building Using a Biologically Inspired Bridge Inspection Robot', Proceedings of the IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, IEEE, Shenyang, China, pp. 1097-1102.View/Download from: UTS OPUS or Publisher's site
This paper presents a Sliding Window approach to viewpoint selection when exploring an environment using a RGB-D sensor mounted to the end-effector of an inchworm climbing robot for inspecting areas inside steel bridge archways which cannot be easily accessed by workers. The proposed exploration approach uses a kinematic chain robot model and information theory-based next best view calculations to predict poses which are safe and are able to reduce the information remaining in an environment. At each exploration step, a viewpoint is selected by analysing the Pareto efficiency of the predicted information gain and the required movement for a set of candidate poses. In contrast to previous approaches, a sliding window is used to determine candidate poses so as to avoid the costly operation of assessing the set of candidates in its entirety. Experimental results in simulation and on a prototype climbing robot platform show the approach requires fewer gain calculations and less robot movement, and therefore is more efficient than other approaches when exploring a complex 3D steel bridge structure.
Paul, G., Quin, P., Yang, C. & Liu, D. 2015, 'Key Feature-Based Approach for Efficient Exploration of Structured Environments', Proceedings of the 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE International Conference on Robotics and Biomimetics, IEEE, Zhuhai, China, pp. 90-95.View/Download from: UTS OPUS or Publisher's site
This paper presents an exploration approach for robots to determine sensing actions that facilitate the building of surface maps of structured partially-known environments. This approach uses prior knowledge about key environmental features to rapidly generate an estimate of the rest of the environment. Specifically, in order to quickly detect key features, partial surface patches are used in combination with pose optimisation to select a pose from a set of nearest neighbourhood candidates, from which to make an observation of the surroundings. This paper enables the robot to greedily search through a sequence of nearest neighbour poses in configuration space, then converge upon poses from which key features can best be observed. The approach is experimentally evaluated and found to result in significantly fewer exploration steps compared to alternative approaches.
Hassan, M., Liu, D., Paul, G. & Huang, S. 2015, 'An Approach to Base Placement for Effective Collaboration of Multiple Autonomous Industrial Robots', Proceedings - IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers (IEEE), Washington State Convention Center in Seattle, Washington, USA, pp. 3286-3291.View/Download from: UTS OPUS or Publisher's site
There are many benefits for the deployment of multiple autonomous industrial robots to carry out a task, particularly if the robots act in a highly collaborative manner. Collaboration can be possible when each robot is able to autonomously explore the environment, localize itself, create a map of the environment and communicate with other robots. This paper presents an approach to the modeling of the collaboration problem of multiple robots determining optimal base positions and orientations in an environment by considering the team objectives and the information shared amongst the robots. It is assumed that the robots can communicate so as to share information on the environment, their operation status and their capabilities. The approach has been applied to a team of robots that are required to perform complete surface coverage tasks such as grit-blasting and spray painting in unstructured environments. Case studies of such applications are presented to demonstrate the effectiveness of the approach.
To, A.W., Paul, G., Rushton-Smith, D., Liu, D. & Dissanayake, G. 2012, 'Automated and Frequent Calibration of a Robot Manipulator-mounted IR Range camera for Steel Bridge Maintenance', Field and Service Robotics Vol 92 - Results of the 8th International Conference on Field and Service Robotics, International Conference on Field and Service Robotics, Springer-Verlag, Matsushima, Miyagi, Japan, pp. 205-218.View/Download from: UTS OPUS or Publisher's site
This paper presents an automated and cost-effective approach to frequent hand-eye calibration of an IR range camera mounted to the end-effector of a robot manipulator for use in a field environment. A set of three reflector discs arranged in a structured pattern attached to the robot platform is used to provide high contrast image features with corresponding range readings for accurate calculation of the camera-to-robot base transform. Using this approach the hand-eye transform between the IR range camera and robot end-effector can be determined by considering the robot manipulator model. Experimental results show that a structured lightingbased IR range camera can be reliably hand-eye calibrated to a 6DOF robot manipulator using the presented automated approach. Once calibrated, the IR range camera can be positioned with the manipulator so as to generate a high resolution geometric map of the surrounding environment suitable for performing the grit-blasting task.
Quin, P.D., Alempijevic, A., Paul, G. & Liu, D. 2014, 'Expanding Wavefront Frontier Detection: An Approach for Efficiently Detecting Frontier Cells', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/b…, Australasian Conference on Robotics and Automation, Australasian Robotics and Automation Association, Melbourne, pp. 1-10.View/Download from: UTS OPUS
Frontier detection is a key step in many robot exploration algorithms. The more quickly frontiers can be detected, the more efficiently and rapidly exploration can be completed. This paper proposes a new frontier detection algorithm called Expanding Wavefront Frontier Detection (EWFD), which uses the frontier cells from the previous timestep as a starting point for detecting the frontiers in the current timestep. As an alternative to simply comparing against the naive frontier detection approach of evaluating all cells in a map, a new benchmark algorithm for frontier detection is also presented, called Naive Active Area frontier detection, which operates in bounded constant time. EWFD and NaiveAA are evaluated in simulations and the results compared against existing state-of-the-art frontier detection algorithms, such as Wavefront Frontier Detection and Incremental-Wavefront Frontier Detection.
Ward, P.K., Quin, P., Pagano, D., Yang, C., Liu, D., Waldron, K., Dissanayake, D., Paul, G., Brooks, P., Mann, P., Kaluarachchi, W., Manamperi, P. & Matkovic, L. 2014, 'Climbing Robot for Steel Bridge Inspection: Design Challenges', Proceedings for the Austroads Publications Online, Austroads Bridge Conference, ARRB Group, New South Wales.View/Download from: UTS OPUS
Inspection of bridges often requires high risk operations such as working at heights, in confined spaces, in hazardous environments; or sites inaccessible by humans. There is significant motivation for robotic solutions which can carry out these inspection tasks. When inspection robots are deployed in real world inspection scenarios, it is inevitable that unforeseen challenges will be encountered.
Since 2011, the New South Wales Roads & Maritime Services and the Centre of Excellence for Autonomous Systems at the University of Technology, Sydney, have been working together to develop an innovative climbing robot to inspect high risk locations on the Sydney Harbour Bridge. Many engineering challenges have been faced throughout the development of several prototype climbing robots, and through field trials in the archways of the Sydney Harbour Bridge. This paper will highlight some of the key challenges faced in designing a climbing robot for inspection, and then present an inchworm inspired robot which addresses many of these challenges.
Sehestedt, S.A., Paul, G., Rushton-Smith, D. & Liu, D. 2013, 'Prior-knowledge Assisted Fast 3D Map Building of Structured Environments for Steel Bridge Maintenance', IEEE International Conference on Automation Science and Engineering, IEEE Conference on Automation Science and Engineering, IEEE, Madison, WI, USA, pp. 1040-1046.View/Download from: UTS OPUS or Publisher's site
Practical application of a robot in a structured, yet unknown environment, such as in bridge maintenance, requires the robot to quickly generate an accurate map of the surfaces in the environment. A consistent and complete map is fundamental to achieving reliable and robust operation. In a real-world and field application, sensor noise and insufficient exploration oftentimes result in an incomplete map. This paper presents a robust environment mapping approach using prior knowledge in combination with a single depth camera mounted on the end-effector of a robotic manipulator. The approach has been successfully implemented in an industrial setting for the purpose of steel bridge maintenance. A prototype robot, which includes the presented map building approach in its software package, has recently been delivered to industry.
Rushton-Smith, D., To, A.W., Paul, G. & Liu, D. 2013, 'An Accurate and Reliable Approach to Calibration of a Robot Manipulator-Mounted IR Range Camera for Field Applications', International Symposium on Robotics and Mechatronics, International Symposium on Robotics and Mechatronics, Research Publishing, Singapore, pp. 335-344.View/Download from: UTS OPUS
Quin, P.D., Paul, G., Alempijevic, A., Liu, D. & Dissanayake, G. 2013, 'Efficient Neighbourhood-Based Information Gain Approach for Exploration of Complex 3D Environments', 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 1343-1348.View/Download from: UTS OPUS or Publisher's site
This paper presents an approach for exploring a complex 3D environment with a sensor mounted on the end effector of a robot manipulator. In contrast to many current approaches which plan as far ahead as possible using as much environment information as is available, our approach considers only a small set of poses (vector of joint angles) neighbouring the robot's current pose in configuration space. Our approach is compared to an existing exploration strategy for a similar robot. Our results demonstrate a significant decrease in the number of information gain estimation calculations that need to be performed, while still gathering an equivalent or increased amount of information about the environment.
Quin, P.D., Paul, G., Liu, D. & Alempijevic, A. 2013, 'Nearest Neighbour Exploration with Backtracking for Robotic Exploration of Complex 3D Environments', Proceedings of Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australian Robotics & Automation Association, Sydney, Australia, pp. 1-8.View/Download from: UTS OPUS
Australasian Conference on Robotics and Automation
Chotiprayanakul, P., Liu, D. & Paul, G. 2011, 'Effect of View Distance and Movement Scale on Haptic-based Teleoperation of a Sand-blasting Robotic System for Complex Steel Bridge Maintenance', Proceedings of the 28th International Symposium on Automation and Robotics in Construction (ISARC 2011), ISARC2011 conference organiser, Seoul, Korea, pp. 1019-1024.
Manamperi, P., Brooks, P.A., Kaluarachchi, W., Peters, G., Ho, A., Lie, S., To, A.W., Paul, G., Rushton-Smith, D., Webb, S.S., Liu, D. & Dissanayake, G. 2011, 'Robotic Grit-blasting: Engineering Challenges', Austroads 8th Bridge Conference: Sustainable Bridges: The Thread of Society, Austroads 8th Bridge Conference: Sustainable Bridges: The Thread of Society, 2011 Austroads Bridge Conference (ABC 2011), Sydney, Australia, pp. 321-330.View/Download from: UTS OPUS
Infrastructure shortage and aging are worldwide issues. Australia, in particular, faces unique challenges in maintaining infrastructures such as roadways and bridges. Corrosion is the primary cause of failure in steel bridges, and is minimised by painting the steel structure. Stripping of rust and deteriorated paint by grit-blasting is an effective and practical method. However, grit-blasting operation is extremely labour intensive and hazardous. It is one of the biggest expenditure items in bridge maintenance operations. Robotics technologies can provide effective solutions to assist bridge maintenance workers in grit blasting. Since 2005, the NSW Roads & Traffic Authority (RTA) and the Centre of Excellence for Autonomous Systems at the University of Technology, Sydney have been working together in developing a robotic system for assisting bridge maintenance workers, with the ultimate objective of preventing human exposure to hazardous and dangerous dust containing rust, paint particles and lead, relieving human workers from labor intensive tasks, and reducing costs associated with bridge maintenance. A prototype robotic system has been developed and tested in both lab setup and on-site. Many engineering issues have been identified for deploying such a system in the field. This paper will present these issues and discuss the solutions.
Richards, D., Paul, G., Webb, S.S. & Kirchner, N.G. 2010, 'Manipulator-based Grasping Pose Selection by means of Task-Objective Optimisation', 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. 1-9.View/Download from: UTS OPUS
This paper presents an alternative to inverse kinematics for mobile manipulator grasp pose selection which integrates obstacle avoidance and joint limit checking into the pose selection process. Given the Cartesian coordinates of an object in 3D space and its normal vector, end-effector pose objectives including collision checking and joint limit checks are used to create a series of cost functions based on sigmoid functions. These functions are optimised using Levenberg-Marquardtâs algorithm to determine a valid pose for a given object. The proposed method has been shown to extend the workspace of the manipulator, eliminating the need for precomputed grasp sets and post pose selection collision checking and joint limit checks. This method has been successfully used on a 6 DOF manipulator both in simulation and in the real world environment.
Kirchner, N.G., Alempijevic, A., Caraian, S.A., Fitch, R., Hordern, D.L., Hu, G., Paul, G., Richards, D., Singh, S.P. & Webb, S.S. 2010, 'RobotAssist - a Platform for Human Robot Interaction Research', 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, pp. 1-10.View/Download from: UTS OPUS
This paper presents RobotAssist, a robotic platform designed for use in human robot interaction research and for entry into Robocup@Home competition. The core autonomy of the system is implemented as a component based software framework that allows for integration of operating system independent components, is designed to be expandable and integrates several layers of reasoning. The approaches taken to develop the core capabilities of the platform are described, namely: path planning in a social context, Simultaneous Localisation and Mapping (SLAM), human cue sensing and perception, manipulatable object detection and manipulation.
Paul, G., Webb, S.S., Liu, D. & Dissanayake, G. 2010, 'A Robotic System for Steel Bridge Maintenance: Field Testing', 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.View/Download from: UTS OPUS
This paper presents the field testing results of an autonomous manipulator-based robotic system that strips the paint and rust from steel bridges [Liu et al., 2008]. The key components of this system are sensing and planning, which have been presented in other research papers. The grit-blasting field trial presented in this paper spanned 6 weeks, and included 20 hours over 4.5 days of actual grit-blasting operation. The field testing has verified the algorithms developed for exploration, mapping, surface segmentation, robot motion planning and collision avoidance. It has also proved that the robotic system is able to perform bridge maintenance operations (grit-blasting), reduce human workers' exposure to hazardous and dangerous debris (containing rust, lead-based paint particles), and relieve workers from labour-intensive tasks. The system has been shown to position a grit-blast nozzle so as to remove the paint and rust at the same rate that is expected of a worker with equivalent equipment: small grit-blasting pot and medium-sized hose nozzle. Testing in the field has also highlighted important issues that need to be addressed.
To, A.W., Paul, G. & Liu, D. 2010, 'Image Segmentation for Surface Material-type Classification using 3D Geometry Information', Proceedings of the 2010 IEEE International Conference on Information and Automation (ICIA2010), IEEE International Conference on Information and Automation, IEEE, Harbin, China, pp. 1717-1722.View/Download from: UTS OPUS or Publisher's site
This paper describes a novel approach for the segmentation of complex images to determine candidates for accurate material-type classification. The proposed approach identifies classification candidates based on image quality calculated from viewing distance and angle information. The required viewing distance and angle information is extracted from 3D fused images constructed from laser range data and image data. This approach sees application in material-type classification of images captured with varying degrees of image quality attributed to geometric uncertainty of the environment typical for autonomous robotic exploration. The proposed segmentation approach is demonstrated on an autonomous bridge maintenance system and validated using gray level cooccurrence matrix (GLCM) features combined with a naive Bayes classifier. Experimental results demonstrate the effects of viewing distance and angle on classification accuracy and the benefits of segmenting images using 3D geometry information to identify candidates for accurate material-type classification.
To, A.W., Paul, G., Kwok, N. & Liu, D. 2008, 'An integrated approach to planning for autonomous grit-blasting robot in complex bridge environments', Proceedings of 2008 Fourth I*PROMS Virtual Conference International Conference on Innovative Production Machines and Systems, International Conference on Innovative Production Machines and Systems, Whittles Publishing, Cardiff University, Wales, UK, pp. 313-318.View/Download from: UTS OPUS
This paper describes an integrated approach to robot manipulator path and motion planning in complex bridge environments. It incorporates grit-blasting specific considerations including blasting effect, coverage, path length and robot arm joint movement. A genetic algorithm is implemented for path planning with the use of environment data to increase planning efficiency. A customized gradient based method is applied in selecting collision free joint configurations for the identified path. A grit-blast coverage model is also developed for discrete non-planar 3D coverage determination to verify the performance of the planned path and motion.
Clifton, M., Paul, G., Kwok, N. & Liu, D. 2008, 'Evaluating performance of multiple RRTs', Proceedings of the IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, American Society of Mechanical Engineering, IEEE, Beijing, China, pp. 564-569.View/Download from: UTS OPUS or Publisher's site
This paper presents experimental results evaluating the performance of a new multiple Rapidly exploring Random Tree (RRT) algorithm. RRTs are randomised planners especially adept at solving difficult, high dimensional path planning problems. However, environments with low-connectivity due to the presence of obstacles can severely affect convergence. Multiple RRTs have been proposed as a means of addressing this issue, however, this approach can adversely affect computational efficiency. This paper introduces a new and simple method which takes advantage of the benefits path of multiple trees, whilst ensuring the computational burden of maintaining them is minimised. Results indicate that multiple RRTs are able to reduce the logarithmic complexity of the search, most notably in environments with high obstacle densities.
Liu, D., Dissanayake, G., Manamperi, P., Fang, G., Paul, G., Kirchner, N.G. & Chotiprayanakul, P. 2008, 'A robotic system for steel bridge maintenance: research challenges and system design', Proceedings of the Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australian Robotics and Automation Association, Australia National University, Canberra, Australia, pp. 1-7.View/Download from: UTS OPUS
Kirchner, N.G., Liu, D., Taha, T. & Paul, G. 2007, 'Capacitive Object Ranging and Material Type Classifying Sensor', Proceedings of the 8th International Conference on Intelligent Technologies (InTech), International Conference on Intelligent Technologies, University of Technology, Sydney, Sydney, Australia, pp. 130-135.View/Download from: UTS OPUS
Kirchner, N.G., Taha, T., Liu, D. & Paul, G. 2007, 'Simultaneous Material Type Classification And Mapping Data Acquisition Using A Laser Range Finder', Proceedings of the 8th International Conference on Intelligent Technologies (InTech), International Conference on Intelligent Technologies, University of Technology, Sydney, Sydney, Australia, pp. 124-129.View/Download from: UTS OPUS
This paper presents a method for single sensor simultaneous derivation of three-dimensional mapping data and material type data for use in an autonomous sandblasting system. A Hokuyo laser range finders firmware has been modified so that it returns intensity data. A range error and return intensity analyzing algorithm allows the material type of the sensed object to be determined from a set of known materials. Empirical results have demonstrated the systems ability to classify material type (under alignment and orientation constraints) from a set of known materials common to sandblasting environments (wood, concrete, metals with different finishes and cloth/fabric) and to successfully classify objects both when static and when fitted to an in-motion 6-DOF anthropomorphic robotic arm.
Paul, G., Liu, D., Kirchner, N.G. & Webb, S.S. 2007, 'Safe and efficient autonomous exploration technique for 3D mapping of a complex bridge maintenance environment', Proceedings of the 24th International Symposium on Automation and Robotics in Construction (ISARC 2007), International Symposium on Automation and Robotics in Construction, Indian Institute of Technology Madras, Kochi, Kerala, India, pp. 99-104.View/Download from: UTS OPUS
Paul, G. & Liu, D. 2006, 'Replanning of Multiple Autonomous Vehicles in Material Handling', 2006 IEEE Conference on Robotics, Automation and Mechatronics, IEEE Conference on Robotics, Automation and Mechatronics, IEEE, Bangkok, Thailand, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
The fully automated docks in Australia present opportunities for applications of autonomous vehicles and engineering innovation. When planning tasks to be done by multi-autonomous vehicles in an enclosed area with a known dynamic map (i.e. bi-directional path network), there are many issues that have not yet been comprehensively solved. The real world presents more complexity than the initial algorithms addressed. There are problems that occur due to interaction with the real-world. This means autonomous vehicles can stop, are affected, or face problems, and hence tasks and vehicles' paths and motion need to be replanned. In order to replan, a greater understanding of the state of vehicles, the state of the map, and importantly the importance of tasks and vehicles is definitely needed. This paper explores the improvements made to replanning by gaining a thorough understanding of the map and then utilising map information to make the best, most efficient replanning decision. Five replanning methods are investigated and four options which combine the methods in different ways are tested in this research. A map analysis method is also presented. Simulation studies show that map information based replanning is the most efficient method out of those tested