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Dr Ravi Ranasinghe

Senior Research Fellow, School of Mechanical and Mechatronic Engineering
Computer Science & Engineering, Electical Engineering
Member, Institute of Electrical and Electronics Engineers
 
Phone
+61 2 9514 2280
Can supervise: Yes

Conferences

Ranasinghe, R. & Kodagoda, S. 2016, 'Spatial Prediction in Mobile Robotic Wireless Sensor Networks withNetwork Constraints', IEEE, IEEE International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.
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Wu, K., Dissanayake, G. & Ranasinghe, R. 2015, 'Active Recognition and Pose Estimation of Household Objects in Clutter', Proceedings of 2015 IEEE International Conference on Robotics and Automation (ICRA), International Conference on Robotics and Automation, IEEE, Seattle, pp. 4230-4237.
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This paper presents an active object recognition and pose estimation system for household objects in a highly cluttered environment. A sparse feature model, augmented with the characteristics of features when observed from different viewpoints is used for recognition and pose estimation while a dense point cloud model is used for storing geometry. This strategy makes it possible to accurately predict the expected information available during the Next-Best-View planning process as both the visibility as well as the likelihood of feature matching can be considered simultaneously. Experimental evaluations of the object recognition and pose estimation with an RGB-D sensor mounted on a Turtlebot are presented.
Tran, A., Liu, D., Ranasinghe, R., Carmichael, M. & Liu, C. 2015, 'Analysis of Human grip strength in physical Human Robot Interaction', Applied Human Factors and Ergonomics, Las Vegas.
The purpose of this paper is to explore how an operator's grip plays a role in physical Human Robot Interaction (pHRI). By considering how the operator reacts to or initiates changes in control, it is possible to study the operator's grip pattern. By analyzing the grip pattern, it is possible to incorporate their natural response in order to create safer and more intuitive interfaces. An experiment where an exoskeleton and human collaborate in order to complete a path following task has been chosen to observe the forces applied by the user at the handle to determine the interaction between the operator and robot. A ThruMode Matrix Array sensor has been wrapped around the robot's handle to measure the applied pressure. By introducing the sensor it not only enables the measurement of the applied forces and how they are applied but also a measure of how tight the user is gripping the handle. Previous studies show that the natural response of a human to an unexpected event is to tighten their grip, indicating that how an operator grasps the handle can be related to the operator's intention. In order to investigate how the operator's grip of the handle changes, the experiments presented in this paper examine two different scenarios which might occur during an interaction, the first where the robot attempts to deviate from the path and the second where the operator wishes to deviate to a new path. The results of the experiments show that whether the operator or the robot initiates the transition, a measurable change in how the operator grasps the handle. The information in this paper can lead to new applications in pHRI by exploring the possible uses of an operator's grasping strength.
Furukawa, T., Dantanarayana, L.I., Ziglar, J., Ranasinghe, R. & Dissanayake, G. 2015, 'Fast Global Scan Matching for High-Speed Vehicle Navigation', IEEE Xplore, Multisensor Fusion and Integration for Intelligent Systems (MFI), 2015 IEEE International Conference on, IEEE, San Diego, CA, USA, pp. 37-42.
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Dantanarayana, L., Dissanayake, G., Ranasinghe, R. & Furukawa, T. 2015, 'An extended Kalman filter for localisation in occupancy grid maps', 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS), IEEE, pp. 419-424.
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Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2014, 'Mobile Robotic Wireless Sensor Networks for Efficient Spatial Prediction', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Chicago, IL, USA, pp. 1176-1181.
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This paper addresses the issue of monitoring physical spatial phenomena of interest utilizing the information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. The spatial phenomenon is statistically modelled by a Gaussian Markov Random Field (GMRF) with hyperparameters that are learnt as the measurements accumulate over time. In this context, the GMRF approximately represents the spatial field on an irregular lattice of triangulation by exploiting a stochastic partial differential equation (SPDE) approach, which benefits remarkably in computation due to the sparsity of the precision matrix. A technique of the one-step-ahead forecast is employed to predict the future measurements that are required to find the optimal sampling locations. It is shown that optimizing the sampling path problem with the logarithm of the determinant either of a covariance matrix using a GP model or of a precision matrix using a GMRF model for mobile robotic wireless sensor networks (MRWSNs) even by a greedy algorithm is impractical. This paper proposes an efficient novel optimality criterion for the adaptive sampling strategy to find the most informative locations in taking future observations that minimize the uncertainty at unobserved locations. The computational complexity of our proposed method is linear, which makes the MRWSN scalable and practically feasible. The effectiveness of the proposed approach is compared and demonstrated using a pre-published data set with appealing results.
Dantanarayana, L., Ranasinghe, R., Tran, A., Liu, D. & Dissanayake, G. 2014, 'A Novel Collaboratively Designed Robot to Assist Carers', SOCIAL ROBOTICS, pp. 105-114.
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Kanzhi, W.U., RANASINGHE, R. & DISSANAYAKE, G. 2014, 'A Fast Pipeline for Textured Object Recognition in Clutter using an RGB-D Sensor', Control Automation Robotics Vision (ICARCV), 2014 13th International Conference on, International Conference on Control, Automation, Robotics and Vision, IEEE, Marina Bay Sands, Singapore, pp. 1650-1655.
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This paper presents a modular algorithm pipeline for recognizing textured household objects in cluttered environment and estimating 6 DOF poses using an RGB-D sensor. The method draws from recent advances in this area and introduces a number of innovations that enable improved performances and faster operational speed in comparison with the state-of-the-art. The pipeline consists of (i) support plane subtraction (ii) SIFT feature extraction and approximate nearest neighbour based matching (iii) feature clustering using 3D Eculidean distances (iv) SVD based pose estimation in combination with a outlier rejection strategy named SORSAC ( Spatially ORdered RAndom Consensus ) and (v) a pose combination and refinement step to combine overlapping identical instances and to refine the pose estimation result by removing incorrect hypothesis. Quantitative comparisons with the MOPED [1] system on self-constructed dataset are presented to demonstrate the effectiveness of the pipeline.
Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2014, 'Spatially-Distributed Prediction with Mobile Robotic Wireless Sensor Networks', 2014 13th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics & Vision, Institute of Electrical and Electronics Engineers Inc., Marina Bay Sands, Singapore, pp. 1153-1158.
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This paper presents a distributed spatial estimation and prediction approach to address the centrally-computed scheme of Gaussian Process regression at each robotic sensor in resource-constrained networks of mobile, wireless and noisy agents monitoring physical phenomena of interest. A mobile sensor independently estimate its own parameters using collective measurements from itself and local neighboring agents as they navigate through the environment. A spatially-distributed prediction algorithm is designed utilizing methods of Jacobi overrelaxation and discrete-time average consensus to enable a robotic sensor to update its estimation of obtaining the global model parameters and recursively compute the global goal of inference. A distributed navigation strategy is also considered to drive sensors to the most uncertain locations enhancing the quality of prediction and learning parameters. Experimental results in a real-world data set illustrate the effectiveness of the proposed approach and is highly comparable to those of the centralized scheme.
Nguyen, V., Kodagoda, S., Ranasinghe, R., Dissanayake, G., Bustamante, H., Vitanage, D. & Nguyen, T. 2014, 'Spatial Prediction of Hydrogen Sulfide in Sewers with a Modified Gaussian Process Combined Mutual Information', 2014 13th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics & Vision, Institute of Electrical and Electronics Engineers Inc., Marina Bay Sands, Singapore, pp. 1130-1135.
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This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H2S) in a gravity sewer system. The gaseous H2S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths of the sewer network. A mutual information based strategy is used to choose the best k sensor measurements and their locations from among n potential sensor observations and their locations. This provably NP-hard combinatorial sensor selection problem is addressed by maximizing the mutual information between the selected locations and the locations that are not selected or do not have any sensor deployments. A proof-of-concept study was carried out comparing the spatial prediction of H2S with a complex model currently used by Sydney Water. The proposed approach is shown to be effective in both modelling and predicting the H2S spatial concentrations in sewers as well as identifying optimal number of H2S sensors and their locations for a required level of prediction accuracy.
Ranasinghe, R., Dantanarayana, L., Tran, A., Lie, S., Behrens, M. & Liu, L. 2014, 'Smart Hoist: An Assistive Robot to Aid Carers', International Conference on Control Automation Robotics & Vision, Control, Automation, IEEE International Conference on Robotics and Vision (ICARCV), IEEE, Singapore, pp. 1285-1291.
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Assistive Robotics(AR) is a rapidly expanding field, implementing advanced intelligent machines which are able to work collaboratively with a range of human users; as assistants, tools and as companions. These AR devices can assist stretched carers at residential aged care facilities to safely enhance their capacity and to improve the quality of care services. The research work presented in this paper describes the pre- liminary outcomes of a design, development and implementation of a patient lifting AR device (Smart Hoist) to reduce lower back injuries to carers while transferring patients in aged care facilities. The proposed solution, a modified conventional lifter device which consists of several sensors capable of interacting with the Smart Hoist operator and its environment, and a set of powered wheels. This solution helps carers to manoeuvre the Smart Hoist safely and intuitively. Preliminary results collected from an evaluation of the Smart Hoist conducted at the premises of IRT Woonona residential care facility confirm the improved safety, comfort and confidence for the carers.
Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2013, 'Locational Optimization based Sensor Placement for Monitoring Gaussian Processes Modeled Spatial Phenomena', Proc. 2013 IEEE 8th Conference on Industrial Electronics and Applications, 2013 IEEE 8th Conference on Industrial Electronics and Applications, IEEE, Melbourne, Australia, pp. 1-6.
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This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expectedvalue function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on realworld datasets have verified the superiority of the proposed approach.
Dantanarayana, L.I., Ranasinghe, R. & Dissanayake, G. 2013, 'C-LOG: A Chamfer Distance Based Method for Localisation in Occupancy Grid-maps', IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE, Tokyo, Japan, pp. 376-381.
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In this paper, the problem of localising a robot within a known two-dimensional environment is formulated as one of minimising the Chamfer Distance between the corresponding occupancy grid map and information gathered from a sensor such as a laser range finder. It is shown that this nonlinear optimisation problem can be solved efficiently and that the resulting localisation algorithm has a number of attractive characteristics when compared with the conventional particle filter based solution for robot localisation in occupancy grids. The proposed algorithm is able to perform well even when robot odometry is unavailable, insensitive to noise models and does not critically depend on any tuning parameters. Experimental results based on a number of public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.
Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2012, 'Simulated Annealing Based Approach for Near-Optimal Sensor Selection in Gaussian Processes', Proc. 2012 IEEE International Conference on Control, Automation and Information Sciences, 2012 IEEE International Conference on Control, Automation and Information Sciences, IEEE, Ho Chi Minh City, Vietnam, pp. 142-147.
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This paper addresses the sensor selection problem associated with monitoring spatial phenomena, where a subset of k sensor measurements from among a set of n potential sensor measurements is to be chosen such that the root mean square prediction error is minimised. It is proposed that the spatial phenomena to be monitored is modelled using a Gaussian Process and a simulated annealing based approximately heuristic algorithm is used to solve the resulting minimisation problem. The algorithm is shown to be computationally efficient and is illustrated using both indoor and outdoor environment monitoring scenarios. It is shown that, although the proposed algorithm is not guaranteed to find the optimum, it always provides accurate solutions for broad range real-world and computer generated datasets.
Wang, S., Kodagoda, S. & Ranasinghe, R. 2012, 'Road Terrain Type Classification based on Laser Measurement System Data', Australasion Conference on Robotics and Automation, Australasion Conference on Robotics and Automation, Australasion Conference on Robotics and Automation, Wellington, New Zealand, pp. 1-6.
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For road vehicles, knowledge of terrain types is useful in improving passenger safety and comfort. The conventional methods are susceptible to vehicle speed variations and in this paper we present a method of using Laser Measurement System (LMS) data for speed independent road type classification. Experiments were carried out with an instrumented road vehicle (CRUISE), by manually driving on a variety of road terrain types namely Asphalt, Concrete, Grass, and Gravel roads at different speeds. A looking down LMS is used for capturing the terrain data. The range data is capable of capturing the structural differences while the remission values are used to observe anomalies in surface reflectance properties. Both measurements are combined and used in a Support Vector Machines Classifier to achieve an average accuracy of 95% on different road types.
Nguyen, V., Ranasinghe, R., Kodagoda, S. & Dissanayake, G. 2012, 'Sensor Selection Based Routing for Monitoring Gaussian Processes Modeled Spatial Phenomena', Australasian Conference on Robotics and Automation 2012, Australasian Conference on Robotics and Automation, The ACRA 2012 Organising Committee, Wellington, New Zealand, pp. 1-7.
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This paper addresses the trade-off between the sensing quality and the energy consumption in the wireless sensor network associated with monitoring spatial phenomena. We use a non-parametric Gaussian Process to model the spatial phenomena to be monitored and simulated annealing based approximately heuristic algorithm for sensor selection. Our novel Sensor Selection based Routing (SSR) algorithm uses this model to identify the most informative nodes, which gives the root mean square prediction error less than a specified threshold, to construct the minimal energy expended routing tree rooted at the sink. Our experiments have verified that the proposed computationally efficient SSR algorithm has significant advantages over conventional techniques.
Shi, L., Kodagoda, S. & Ranasinghe, R. 2011, 'Fast Indoor Scene Classification Using 3D Point Clouds', Australasian Conference on Robotics and Automation 2011, Australasian Conference on Robotics and Automation, The ACRA 2011 Organising Committee, Monash University, Melbourne Australia, pp. 1-7.
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A representation of space that includes both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Identifying and categorizing environments based on onboard sensors are essential in these scenarios. The Kinectâ¢, a 3D low cost sensor is appealing in these scenarios as it can provide rich information. The downside is the presence of large amount of information, which could lead to higher computational complexity. In this paper, we propose a methodology to efficiently classify indoor environments into semantic categories using Kinect⢠data. With a fast feature extraction method along with an efficient feature selection algorithm (DEFS) and, support vector machines (SVM) classifier, we could realize a fast scene classification algorithm. Experimental results in an indoor scenario are presented including comparisons with its counterpart of commonly available 2D laser range finder data.
Dissanayake, G., Huang, S., Wang, Z. & Ranasinghe, R. 2011, 'A Review of Recent Developments in Simultaneous Localization and Mapping', Proceedings of the 6th International Conference on Industrial and Information Systems, International Conference on Industrial and Information Systems, IEEE, Sri Lanka, pp. 477-482.
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Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than a decade. Many fundamental and practical aspects of SLAM have been addressed and some impressive practical solutions have been demonstrated. The aim of this paper is to provide a review of the current state of the research on feature based SLAM, in particular to examine the current understanding of the fundamental properties of the SLAM problem and associated issues with the view to consolidate recent achievements.
Ranasinghe, R.S., Andrew, L.L.H., Hayes, D.A. & Everitt, D. 2001, 'Scheduling disciplines for multimedia WLANs: Embedded round robin and wireless dual queue', IEEE International Conference on Communications, pp. 1243-1248.
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Wireless local area networks have developed into a promising solution to support advanced data services in untethered environments. Selection of an efficient packet-scheduling scheme is important for managing the bandwidth while satisfying QoS requirements of active sessions having diverse traffic characteristics. The key difficulty is the distributed nature of the queues in the uplink, resulting in the scheduler having to trade off polling greedy stations against wasting resources by polling potentially idle stations. In order to address this, we propose a novel scheduling scheme, "Embedded Round Robin", which dynamically classifies stations as "busy" and "clear". We then extend the recently proposed Dual Queue scheduling discipline to the case of wireless networks.
Ranasinghe, R.S., Andrew, L.L.H. & Everitt, D. 1999, 'Impact of polling strategy on capacity of 802.11 based wireless multimedia LANs', IEEE International Conference on Networks, ICON, pp. 96-103.
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Wireless local area networks are a viable technology to support multimedia traffic. One of the prominent wireless local area network standards being adopted as a mature technology is the IEEE 802.11 standard. In wireless multimedia networks, mobile stations will be capable of generating a heterogeneous traffic mix and therefore it is crucial to devise an efficient bandwidth allocation scheme to satisfy the quality of service requirements of each traffic class. In this paper we present a distributed fair queuing scheme which is compatible with the 802.11 standard and can manage bandwidth allocation for delay-sensitive traffic. The performance of the proposed scheme is evaluated by simulation, showing that a distributed version of deficit round robin outperforms the standard round robin service discipline from a capacity viewpoint. © 1999 IEEE.

Journal articles

Nguyen, L.V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2017, 'Adaptive Placement for Mobile Sensors in Spatial Prediction under Locational Errors', IEEE Sensors Journal, vol. 17, no. 3, pp. 794-802.
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Nguyen, L.V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2016, 'Information-Driven Adaptive Sampling Strategy for Mobile Robotic Wireless Sensor Network', IEEE Transactions on Control Systems Technology, vol. 24, no. 1, pp. 372-379.
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This brief addresses the issue of monitoring physical spatial phenomena of interest using information collected by a resource-constrained network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. We first propose an efficient novel optimality criterion for designing a sampling strategy to find the most informative locations in taking future observations to minimize the uncertainty at all unobserved locations of interest. This solution is proven to be within bounds. The computational complexity of this proposition is shown to be practically feasible. We then prove that under a certain condition of monotonicity property, the approximate entropy at resulting locations obtained by our proposed algorithm is within 1-(1/e) of the optimum, which is then utilized as a stopping criterion for the sampling algorithm. The criterion enables the prediction results to be within user-defined accuracies by controlling the number of mobile sensors. The effectiveness of the proposed method is illustrated using a prepublished data set.
Nguyen, L.V., Kodagoda, S. & Ranasinghe, R. 2016, 'Spatial Sensor Selection via Gaussian Markov Random Fields', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 9, pp. 1226-1239.
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© 2013 IEEE. This paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in predicting spatial phenomena by using a wireless sensor network. The spatial field is modeled by Gaussian Markov random fields (GMRFs), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information (MI) between random variables at selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF-based optimality criterion is then proven to be computationally and efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly, with demonstrations of monotonicity and submodularity properties of the MI set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within ${(1-1/e)}$ of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.
Dantanarayana, L., Dissanayake, G. & Ranasinge, R. 2016, 'C-LOG: A Chamfer distance based algorithm for localisation in occupancy grid-maps', CAAI Transactions on Intelligence Technology, vol. 1, no. 3, pp. 272-284.
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