Besinger, A, Sztynda, T, Lal, S, Duthoit, C, Agbinya, J, Jap, B, Eager, D & Dissanayake, G 2010, 'Optical flow based analyses to detect emotion from human facial image data', EXPERT SYSTEMS WITH APPLICATIONS, vol. 37, no. 12, pp. 8897-8902.
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Artificial recognition of facial expression has attracted a lot of attention in the last few years and different facial expression detection methods have been developed. The current study uses a feature point tracking technique separately applied to the five facial image regions (eyebrows, eyes and mouth) to capture basic emotions. The used dataset contains a total 60 facial images from subject's different genders and nationality not wearing glasses and/or facial hair. Results show that the used point tracking algorithm separately applied to the five facial image regions can detect emotions in image sequences. © 2010 Elsevier Ltd. All rights reserved.
Cheong, CY, Tan, KC, Liu, DK & Lin, CJ 2010, 'Multi-objective and prioritized berth allocation in container ports', Annals of Operations Research, vol. 180, no. 1, pp. 63-103.
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This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of makespan, waiting time, and degree of deviation from a predetermined priority schedule. These objectives represent the interests of both port and ship operators. Unlike most existing approaches in the literature which are single-objective-based, a multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with three primary features which are specifically designed to target the optimization of the three objectives. The features include a local search heuristic, a hybrid solution decoding scheme, and an optimal berth insertion procedure. The effects that each of these features has on the quality of berth schedules are studied. © 2008 Springer Science+Business Media, LLC.
Howarth, B, Katupitiya, J, Eaton, R & Kodagoda, S 2010, 'A machine learning approach to crop localisation using spatial information', International Journal of Computer Applications in Technology, vol. 39, no. 1/2/3, pp. 101-101.
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This paper describes an approach to recognise and localise centres of mature lettuce heads in the field when the lettuce leaves obscure the distinctions between plants. This is of great value when using an automatic harvester in cluttered or closely planted vegetation. The aim of this work is to investigate and verify the potential use of spatial rather than visual clues for recognition and localisation, with a view to implement a more robust and sophisticated system if promise is shown. Colour/texture information was difficult to use so spatial information was used instead. A laser range finder was used to generate a height plot from above the plants. Lettuce examples were used to learn the radial distribution of the lettuce model. This was compared with the distributions of arbitrary locations in new scans to locate possible lettuce locations. Planting distance information was then used to localise the final lettuce positions. The algorithm was able to successfully locate 15 out of 16 sample lettuces. © 2010 Inderscience Enterprises Ltd.
Kodagoda, S & Zhang, Z 2010, 'Multiple Sensor-Based Weed Segmentation', Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 224, no. 7, pp. 799-810.
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Bidens pilosa L (commonly known as cobbler's peg) is an annual broad-leaf weed in tropical and subtropical regions and reportedly needs to be identified and eliminated when farming 31 different crop varieties. This paper presents a multi-modal sensing approach for detecting Bidens leaves within wheat plants. Visual cue-based automatic discrimination of Bidens and wheat leaves is non-trivial owing to the curled-up nature of the wheat leaves. Therefore, spectral responses of Bidens and wheat leaves are first analysed to understand the discriminative spectral bands. Then a multi-modal sensory system consisting of a near infra red (NIR) and a visual camera set-up is proposed. Information retrieved from the sensory set up is then processed to generate a series of cues that are fed into a classification algorithm. Classification results are validated through experimentation. The proposed technique is able to achieve an accuracy of 88—95 per cent even when there is substantial overlapping between Bidens and wheat leaves. Further, it is also shown that the algorithm is robust enough to discriminate some other commonly available plant species.
Meng, X, Wang, S, Qiu, J, Zhu, JG, Wang, Y, Guo, Y, Liu, D & Xu, W 2010, 'Dynamic Multilevel Optimization of Machine Design and Control Parameters Based on Correlation Analysis', IEEE Transactions on Magnetics, vol. 46, no. 8, pp. 2779-2782.
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In this paper, a multilevel optimization method is proposed for a motor drive system including a surface-mounted permanent magnet synchronous machine (SPMSM), the converter/inverter, and the control schemes. First, the multilevel optimization is described by using the problem matrix which may be used to allocate the design variables on different levels. The parameters in the problem matrix are deduced by using correlation analysis. Second, the architecture and implementation of multilevel genetic algorithm (MLGA) are carried out. As one of the advantages of MLGA, the dynamic adjustment strategy of GA operators is utilized to improve the optimal performance. The algorithm is then applied to a three-level optimization problem in which the optimization of SPMSM design and the control parameters of drive are considered in different levels. Finally, some results and discussions about the application of the proposed algorithm are presented. © 2006 IEEE.
Sankaran, S 2010, 'Construction Stakeholder Management', CONSTRUCTION ECONOMICS AND BUILDING, vol. 10, no. 1-2.
Su, SW, Huang, S, Wang, L, Celler, BG, Savkin, AV, Guo, Y & Cheng, TM 2010, 'Optimizing Heart Rate Regulation for Safe Exercise', ANNALS OF BIOMEDICAL ENGINEERING, vol. 38, no. 3, pp. 758-768.
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Safe exercise protocols are critical for effective rehabilitation programs. This paper aims to develop a novel control strategy for an automated treadmill system to reduce the danger of injury during cardiac rehabilitation. We have developed a control-oriented nonparametric Hammerstein model for the control of heart rate during exercises by using support vector regression and correlation analysis. Based on this nonparametric model, a model predictive controller has been built. In order to guarantee the safety of treadmill exercise during rehabilitation, this new automated treadmill system is capable of optimizing system performance over predefined ranges of speed and acceleration. The effectiveness of the proposed approach was demonstrated with six subjects by having their heart rate track successfully a predetermined heart rate. © 2009 Biomedical Engineering Society.
Vidal-Calleja, TA, Sanfeliu, A & Andrade-Cetto, J 2010, 'Action Selection for Single-Camera SLAM', IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 6, pp. 1567-1581.
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A method for evaluating, at video rate, the quality of actions for a single camera while mapping unknown indoor environments is presented. The strategy maximizes mutual information between measurements and states to help the camera avoid making ill-conditioned measurements that are appropriate to lack of depth in monocular vision systems. Our system prompts a user with the appropriate motion commands during 6-DOF visual simultaneous localization and mapping with a handheld camera. Additionally, the system has been ported to a mobile robotic platform, thus closing the control-estimation loop. To show the viability of the approach, simulations and experiments are presented for the unconstrained motion of a handheld camera and for the motion of a mobile robot with nonholonomic constraints. When combined with a path planner, the technique safely drives to a marked goal while, at the same time, producing an optimal estimated map. © 2010 IEEE.
Yu, Y-H, Vo-Ky, C, Kodagoda, S & Ha, QP 2010, 'FPGA-Based Relative Distance Estimation for Indoor Robot Control Using Monocular Digital Camera', Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 14, no. 6, pp. 714-721.
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Distance measurement methodologies based on the digital camera usually require time-consuming calibration procedures, some are even derived from complicated image processing algorithms resulting in low picture frame rates. In a dynamic camera system, due to the unpredictability of intrinsic and extrinsic parameters, odometric results are highly dependent on the quality of extra sensors. In this paper, a simple and efficient algorithm is proposed for relative distance estimation in robotic active vision by using a monocular digital camera. Accuracy of the estimation is achieved by judging the 2D perspective projection image ratio of the robot labels obtained on a TFT-LCD (Thin Film Transistor – Liquid Crystal Display) monitor without the need of any additional sensory cost and complicated calibration effort. Further, the proposed algorithm does not contain any trigonometric functions so that it can be easily implemented on an embedded system using the Field Programmable Gate Array (FPGA) technology. Experimental results are included to demonstrate the effectiveness of the technique.
Zhang, Z, Kodagoda, S, Ruiz, D, Katupitiya, J & Dissanayake, G 2010, 'Classification of Bidens in wheat farms', International Journal of Computer Applications in Technology, vol. 39, no. 1/2/3, pp. 123-123.
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Zhang, Z, Kodagoda, S, Ruiz, D, Katupitiya, J & Dissanayake, G 2010, 'Classification of Bidens in wheat farms', International Journal of Computer Applications in Technology, vol. 39, no. 1/2/3, pp. 123-123.
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Bidens pilosa L. (commonly known as cobbler’s peg) is an annual broad leaf weed widely distributed in tropical and subtropical regions of the world and is reported to be a weed of 31 crops, including wheat. Automatic detection of Bidens in wheat farms is a non-trivial problem due to their similarity in colour and presence of occlusions. This paper proposes a methodology which could be used to discriminate Bidens from wheat to be used in operations such as autonomous weed destruction. A spectrometer is used to analyse the optical properties of Bidens and wheat leaves while achieving high classification results. However, due to the practical constraints of using spectrometers, a colour camera-based technique is proposed. It is shown that the colour-based segmentation followed by shape-based validation algorithm gives rise to high detection rates with lower false detections. We have experimentally evaluated the algorithm with Bidens detection rate of 80% and a false alarm rate of 10%. © 2010 Inderscience Enterprises Ltd.
Ahmad, A, Huang, S & Dissanayake, G 1970, 'Accurate large-scale bearing-only SLAM by map joining', Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, Proceedings of the Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Brisbane, Queensland, Australia, pp. 1-10.
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This paper presents a bearing-only SLAM algorithm that generates accurate and consistent maps of large environments by joining a series of small local maps. The local maps are built by least squares optimization with a proper landmark initialization technique. The local maps are then combined to build global map using Iterated Sparse Local Submap Joining Filter (I-SLSJF). The accuracy and consistency of the proposed algorithm is evaluated using simulation data sets. The algorithm is also tested using the DLR-Spatial-Cognition data set and the preprocessed Victoria Park data where the range information is ignored. The global map results are very similar to the result of full least squares optimization starting with very accurate initial values. As I-SLSJF is able to join a given set of local maps and associated uncertainties efficiently without any information loss, these results demonstrate that focusing on generating accurate local maps is a promising direction for solving large-scale bearing-only SLAM problems.
Behrens, M, Huang, S, Dissanayake, G & IEEE 1970, 'Models for pushing objects with a mobile robot using single point contact', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 2964-2969.
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In many mobile robotic manipulation tasks it is desirable to interact with the robots surroundings without actually grasping the object being manipulated. Non-prehensile manipulation allows a robot to interact in situations which would otherwise be impossible due to size or weight. This paper presents the derivation of a mathematical model of an object pushed by a single point and sliding in the presence of friction where the dynamic effects of mass and inertia are significant. This model is validated using numerical simulation. The derived dynamic model is also compared with a kinematic approximation from literature, showing that under certain conditions, the motion of a pushed object is similar to the motion of a non-holonomic vehicle. Finally, the results of experimental investigations are discussed and promising directions for further work are proposed. ©2010 IEEE.
Carmichael, MG, Liu, D & Waldron, KJ 1970, 'Investigation of reducing fatigue and musculoskeletal disorder with passive actuators', 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), IEEE, Taipei, Taiwan, pp. 2481-2486.
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Robotic systems such as exoskeletons can be effectively used in the reduction of fatigue and musculoskeletal disorders (MSD) associated with physical tasks, but robots which work in physical contact with humans pose problems with user safety. A novel approach to developing intrinsically safe robots is to use passive actuators which have the advantage of being safer, ensuring stability, high force/weight ratios and lower power consumption. It is however not clear how effective an exoskeleton utilizing passive actuators would be in reducing fatigue and the risk of MSD. This paper analyzes the benefit of using such a system with results from dynamic simulations and an experiment using a specially designed mechanism used for evaluation. Results indicate that fatigue and effort could be reduced if robot impedance is minimized. Experiments also highlighted issues of implementing such a system into practice. ©2010 IEEE.
Csonka, PJ, Perkins, AD & Waldron, KJ 1970, 'Passively stable hopping of an articulated leg with a tendon-coupled ankle', 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), IEEE, Taipei, Taiwan, pp. 3629-3633.
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Dynamic maneuvers have been successfully implemented on many prismatic legged robots. Systems with articulated legs of significant relative mass pose more of a challenge in part due to the physics of thrusting with rotating limbs, which results in undesi
Hu, G, Huang, S, Dissanayake, G & IEEE 1970, 'Evaluation of Pose Only SLAM', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 3732-3737.
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In recent SLAM (simultaneous localization and mapping) literature, Pose Only optimization methods have become increasingly popular. This is greatly supported by the fact that these algorithms are computationally more efficient, as they focus more on the robots trajectory rather than dealing with a complex map. Implementation simplicity allows these to handle both 2D and 3D environments with ease. This paper presents a detailed evaluation on the reliability and accuracy of Pose Only SLAM, and aims at providing a definitive answer to whether optimizing poses is more advantages than optimizing features. Focus is centered around TORO, a Tree based network optimization algorithm, which has gained increased recognition within the robotics community. We compare this with Least Squares, which is often considered one of the best Maximum Likelihood method available. Results are based on both simulated and real 2D environments, and presented in a way where our conclusions can be substantiated. ©2010 IEEE.
Huang, S, Lai, Y, Frese, U, Dissanayake, G & IEEE 1970, 'How far is SLAM from a linear least squares problem?', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 3011-3016.
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Most people believe SLAM is a complex nonlinear estimation/optimization problem. However, recent research shows that some simple iterative methods based on linearization can sometimes provide surprisingly good solutions to SLAM without being trapped into a local minimum. This demonstrates that hidden structure exists in the SLAM problem that is yet to be understood. In this paper, we first analyze how far SLAM is from a convex optimization problem. Then we show that by properly choosing the state vector, SLAM problem can be formulated as a nonlinear least squares problem with many quadratic terms in the objective function, thus it is clearer how far SLAM is from a linear least squares problem. Furthermore, we explain that how the map joining approaches reduce the nonlinearity/nonconvexity of the SLAM problem. ©2010 IEEE.
Killen, CP, Levin, G, Kwak, YH & Sankaran, S 1970, 'Project Portfolio Management (PPM) - Strategic and Operational Agility Through Projects', PMI® Research & Education Conference 2010: Defining the Future of Project Management, Defining the Future of Project Management - Research & Education Conference 2010, Project Management Institute (PMI), Washington D.C., USA, pp. 1-1.
Kirchner, N, Alempijevic, A, Caraian, S, Fitch, R, Hordern, D, Hu, G, Paul, G, Richards, D, Singh, SPN & Webb, S 1970, 'RobotAssist - A platform for human robot interaction research', Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, Proceedings of the Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Brisbane, pp. 1-10.
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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.
Kodagoda, S, Zhang, Z & Dissanayake, G 1970, 'Crop and weed classification based on a colour and NIR sensory setup', Proc. Innovative Production Machines and Systems - 5th I*PROMS Virtual International Conference, International Conference on Innovative Production Machines and Systems, Whittles Publishing, Virtual conference, pp. 212-217.
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This paper presents a multi-modal sensing approach for detecting Bidens pilosa L (commonly known as cobbler's peg) in wheat farms. Bidens is an annual broad leaf weed in tropical and sub-tropical regions and reported to be a weed that needs to be identified and eliminated when farming thirty one different crop varieties. Both Bidens and wheat leaves can have similar visual cues due to the curled up nature of the wheat leaves. This makes a straightforward visual image (RGB) based classification nontrivial. Therefore, we have integrated another informative band in the spectrum, which is the NIR band. Information retrieved is processed to generate a series of cues that are then fed into a classification algorithm. Bidens and wheat plant species are used to verify the classification algorithm. The proposed technique is able to achieve an accuracy of 88% - 95% even when there is substantial overlapping between Bidens and wheat leaves.
Liu, M, Huang, S, Dissanayake, G, Kodagoda, S & IEEE 1970, 'Towards a Consistent SLAM Algorithm using B-Splines to Represent Environments', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 2065-2070.
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This paper presents a statistically consistent SLAM algorithm where the environment is represented using a collection of B-Splines. The use of B-Splines allow environment to be represented without having to extract specific geometric features such as lines or points. Our previous work proposed a new observation model that enables raw measurements taken from a laser range finder to be transferred into relative position information between the control points of a B-Spline and the robot pose where the observation is made. One of the unresolved issues in the work was the estimation of the observation covariance, which is addressed through an analytical approach in this paper. As the uncertainty associated with the observation model is accurately defined and an optimization approach is used in the estimation process, the proposed SLAM algorithm can produce consistent estimates. Both simulation and experimental data are used for evaluation of the results. ©2010 IEEE.
Miro, JV & Dissanayake, G 1970, 'Automatic fine motor control behaviours for autonomous mobile agents operating on uneven terrains', Proceedings of the 3rd International Symposium on Practical Cognitive Agents and Robots, PCAR '10: 2010 International Symposium on 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. Copyright 2010 ACM.
Miro, JV, Dumonteil, G, Beck, C, Dissanayake, G & IEEE 1970, 'A Kyno-dynamic Metric to Plan Stable Paths Over Uneven Terrain', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 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. ©2010 IEEE.
Patel, M, Khushaba, R, Miro, JV & Dissanayake, G 1970, 'Probabilistic models versus discriminate classifiers for human activity recognition with an instrumented mobility-assistance aid', Proceedings of the 2010 Australasian Conference on Robotics and Automation, 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.
Patel, M, Miro, JV & Dissanayake, G 1970, 'Dynamic Bayesian Networks for Learning Interactions between Assistive Robotic Walker and Human Users', 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. © 2010 Springer-Verlag Berlin Heidelberg.
Paul, G, Webb, S, Liu, D & Dissanayake, G 1970, 'A robotic system for steel bridge maintenance: Field testing', Proceedings of the 2010 Australasian Conference on Robotics and Automation, 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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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.
PERKINS, AD, CSONKA, PJ & WALDRON, KJ 1970, 'ROBOTIC HOPPING WITH A BIOMIMETIC LEG', Emerging Trends in Mobile Robotics, Proceedings of the 13th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, WORLD SCIENTIFIC, pp. 125-132.
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© 2010 by World Scientific Publishing Co. Pte. Ltd. There are many legged robots which perform well statically, but none that can execute robust dynamic maneuvers. One limitation of current systems lies in their leg designs, which are not well suited to dynamic maneuvering. A new leg is designed based on consideration of four areas: joint type, foot design, actuation type, and mechanical coupling. This leg has an articulated hip, knee, and ankle, a substantial (non-point) foot, pneumatic actuation at the knee, and mechanical coupling between the knee and ankle joints. This leg is attached to a torso to form a monopod, and produces stable hopping in simulation, which is confirmed in initial experimentation.
PETERS, G, PAGANO, D, LIU, DK & WALDRON, K 1970, 'A PROTOTYPE CLIMBING ROBOT FOR INSPECTION OF COMPLEX FERROUS STRUCTURES', Emerging Trends in Mobile Robotics, Proceedings of the 13th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, WORLD SCIENTIFIC, Nagoya, Japan, pp. 150-156.
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© 2010 by World Scientific Publishing Co. Pte. Ltd. Currently many hazardous maintenance and inspection tasks, such as paint inspection and corrosion condition monitoring of steel structures, are being performed manually by workers, which causes serious health and safety problems. This paper presents a concept climbing robot, with the aim of exploring highly complex ferrous structures such as steel bridges, for the purpose of inspection duties. To demonstrate this concept, a quadruped prototype is developed. A modular architecture that simplifies the development process and improves reusability has been implemented. Permanent magnet compliant pads on each foot provide a simple method of adhesion on the highly complex and unsmooth surface of a bridge. A simple detachment mechanism has been employed. Experiments have been conducted to prove the concept and test the design of the prototype.
Richards, D, Paul, G, Webb, S & Kirchner, N 1970, 'Manipulator-based grasping pose selection by means of task-objective optimisation', Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, Proceedings of the Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Brisbane, Queensland, Australia, pp. 1-9.
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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-Marquardt's 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.
Riedy, C & Sankaran, S 1970, 'Identifying sustainable futures: Threats to Indian Ocean sustainability and possible responses', Indian Ocean and South Asia Research Network (IOSARN) Conference, University of Technology, Sydney, Australia.
Sehestedt, S, Kodagoda, S & Dissanayake, G 1970, 'Robot path planning in a social context', 2010 IEEE Conference on Robotics, Automation and Mechatronics, 2010 IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Singapore, pp. 206-211.
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Human robot interaction has attracted significant attention over the last couple of years. An important aspect of such robotic systems is to share the working space with humans and carry out the tasks in a socially acceptable way. In this paper, we address the problem of fusing socially acceptable behaviours into robot path planning. By observing an environment for a while, the robot learns human motion patterns based on sampled Hidden Markov Models and utilises them in a Probabilistic Roadmap based path planning algorithm. This will minimise the social distractions, such as going through someone else's working space (due to the shortest path), by planning the path through minimal distractions, leading to human-like behaviours. The algorithm is implemented in Orca/C++ with appealing results in real world experiments. ©2010 IEEE.
Sehestedt, S, Kodagoda, S, Dissanayake, G & IEEE 1970, 'Models of Motion Patterns for Mobile Robotic Systems', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 4127-4132.
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Human robot interaction is an emerging area of research with many challenges. Knowledge about human behaviors could lead to more effective and efficient interactions of a robot in populated environments. This paper presents a probabilistic framework for the learning and representation of human motion patterns in an office environment. It is based on the observation that most human trajectories are not random. Instead people plan trajectories based on many considerations, such as social rules and path length. Motion patterns are learned using an incrementally growing Sampled Hidden Markov Model. This model has a number of interesting properties which can be of use in many applications. For example, the learned knowledge can be used to predict motion, infer social rules, thus improve a robot's operation and its interaction with people in a populated space. The proposed learning method is extensively validated in real world experiments. ©2010 IEEE.
Sehestedt, S, Kodagoda, S, Dissanayake, G & IEEE 1970, 'Using Common Motion Patterns to Improve a Robot's Operation in Populated Environments', 11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), Int. Conf. Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 2036-2041.
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Robotic devices are increasingly penetrating the human work spaces as stand alone units and helpers. It is believed that a robot could be easily integrated with humans, if the robot can learn how to behave in a socially acceptable manner. This involves a robot to observe, learn and comply with basic rules of human behaviors. As an example, one would expect a robot to travel in an environment without intruding human workspaces unnecessarily. Thus, identifying common motion patterns of people by observing a specific environment is an important task as people's trajectories are usually not random, however are tailored to the way the environment is structured. We propose a learning algorithm to construct a Sampled Hidden Markov Model (SHMM) that captures behavior of people through observations and then demonstrate how this model could be exploited for planning socially aware paths. Experimental results are presented to demonstrate the viability of the proposed approach. ©2010 IEEE.
Shi, L, Kodagoda, S & Dissanayake, G 1970, 'Environment Classification and Semantic Grid Map Building Based on Laser Range Finder Data', IROS 2010 Workshop on Semantic Mapping and Autonomous Knowledge Acquisition, Workshop on Semantic Mapping and Autonomous Knowledge Acquisition, online proceeding of IROS 2010 workshop, Taipei, pp. 1-6.
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Human robot interaction has become an important area of research in the robotics community. High level abstractions, which are commonly used by humans, can be learnt by robots to effectively communicate with humans. In this paper, we propose a Semantic Grid Map (SGM) to represent an environment. SGM is similar to an Occupancy Grid (OG) map, however with high level information as environment type labels. We use a robot-mounted laser range finder (LRF) data to learn and classify an environment into various area types. Then the classification results are combined probabilistically to update the semantic grid map. The classification accuracy is further improved by outlier rejection and topological correction. Finally we present a labeling strategy while a robot is exploring an unknown environment. Experimental results of a robot exploring in a university environment are presented to assess the performance of the algorithm.
Shi, L, Kodagoda, S, Dissanayake, G & IEEE 1970, 'Laser Range Data Based Semantic Labeling of Places', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, Taiwan, pp. 5941-5946.
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Extending metric space representations of an environment with other high level information, such as semantic and topological representations enable a robotic device to efficiently operate in complex environments. This paper proposes a methodology for a robot to classify indoor environments into semantic categories. Classification task, using data collected from a laser range finder, is achieved by a machine learning approach based on the logistic regression algorithm. The classification is followed by a probabilistic temporal update of the semantic labels of places. The innovation here is that the new algorithm is able to classify parts of a single laser scan into different semantic labels rather than the conventional approach of gross categorization of locations based on the whole laser scan. We demonstrate the effectiveness of the algorithm using a data set available in the public domain. ©2010 IEEE.
Shi, L, Kodagoda, S, Dissanayake, G & IEEE 1970, 'Multi-class Classification for Semantic Labeling of Places', 11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), International Conference on Control, Automation, Robotics & Vision, IEEE, Singapore, pp. 2307-2312.
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Human robot interaction is an emerging area of research, where human understandable robotic representations can play a major role. Knowledge of semantic labels of places can be used to effectively communicate with people and to develop efficient navigation solutions in complex environments. In this paper, we propose a new approach that enables a robot to learn and classify observations in an indoor environment using a labeled semantic grid map, which is similar to an Occupancy Grid like representation. Classification of the places based on data collected by laser range finder (LRF) is achieved through a machine learning approach, which implements logistic regression as a multi-class classifier. The classifier output is probabilistically fused using independent opinion pool strategy. Appealing experimental results are presented based on a data set gathered in various indoor scenarios. ©2010 IEEE.
To, AWK, Paul, G & Liu, D 1970, 'Image segmentation for surface material-type classification using 3D geometry information', The 2010 IEEE International Conference on Information and Automation, 2010 International Conference on Information and Automation (ICIA), IEEE, Harbin, China, pp. 1717-1722.
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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 co-occurrence 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. ©2010 IEEE.
Wang, Z, Dissanayake, G & IEEE 1970, 'Map-aided 6-DOF Relative Pose Estimation for Monocular SLAM using Sparse Information Filters', 11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), Int. Conf. Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 1006-1011.
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This paper addresses the problem of mapping three dimensional environments from a sequence of images taken by a calibrated camera, and simultaneously generating the camera motion trajectory. This is the Monocular SLAM problem in robotics, and is akin to the Structure from Motion (SFM) problem in computer vision. We present a novel map-aided 6-DOF relative pose estimation method based on a new formulation of the Monocular SLAM that is able to provide better initial estimates of new camera poses than the simple triangulation traditionally used in this context. The '6-DOF' means relative to the map which itself is up to an unobservable scale. The proposed pose estimator also allows more effective outlier rejection in matching features present in the map and features extracted from two consecutive images. Our Monocular SLAM algorithm is able to deal with arbitrary camera motion, making the smooth motion assumption, which is required by the typically used constant velocity model, unnecessary. In the new Monocular SLAM formulation, the measurements of extracted features from images are partitioned into those used for the estimation of the environment and those used for estimating the camera motion. The new formulation enables the current map estimate to aid achieving the full 6-DOF relative pose estimation up to the mapping scale while maximally exploiting the geometry information in images. Experiment results are provided to verify the proposed algorithm.
Yu, Y-H, Kodagoda, S & Ha, QP 1970, 'FPGA-Based Ubiquitous Computing Intelligence for Robotic Formation Control', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 27th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Bratislava, Slovakia, pp. 193-201.
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Ubiquitous computing (UC) has been an important stream in distributed computing, which is nowadays recognized as an attractive vision in ambient intelligence (AmI) technologies and home robotic systems. This paper, focusing on our work on the application of field programmable gate array (FPGA) in mobile robotics, presents a prototypical computing node of a ubiquitous robot (Ubibot) for indoor multiple robot coordination. The hardware based FGPA designs such as colour discrimination, object tracking, relative distance estimation, and robotic steering manoeuvre are integrated into a single chip. This hardware design, based on the system-on-programmable chip concept, will demonstrate the feasibility of an AmI environment for real-time processing with lower power consumption.
Yu, Y-H, Kodagoda, S, Ha, QP & IEEE 1970, 'Slope-Based Point Pursuing Maneuvers of Nonholonomic Robots using FPGA', IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Taipei, taiwan, pp. 3694-3699.
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Steering maneuver is essential in robotic motion planning. Despite a lot of steering mechanisms successfully developed in past years, for miniature robots, real-time computation is still a limitation for robot path tracking. The design issues in cooperative control of battery-powered nonholonomic robots rest with the complicacy of the control strategies, the low power consumption and real-time processing capability. Conventionally, the improvement of computing speed mostly relies on the increment of the system clock and often results in some transient loss. Thus, an elaborate control algorithm developed for PC might not work on an embedded system. This paper presents a comprehensive steering algorithm which, via issuing predicaments for computation, will dramatically reduce the resource usage in hardware circuit design. The proposed algorithm is implemented on an embedded system for ubiquitous robotics using the field programmable gate array (FPGA) technology. ©2010 IEEE.
Zainudin, Z, Kodagoda, S & Dissanayake, G 1970, 'Torso detection and tracking using a 2D laser range finder', Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, Proceedings of the Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Queensland University of Technology, Brisbane, QueenslandE, Australia, pp. 1-6.
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Detecting and tracking people in populated environments has various applications including, robotics, healthcare, automotive, security and defence. In this paper, we present an algorithm for people detection and tracking based on a two dimensional laser rage finder (LRF). The LRF was mounted on a mobile robotic platform to scan a torso section of a person. The tracker is designed to discard spurious targets based on the log likelihood ratio and can effectively handle short term occlusions. Long term occlusions are considered as new tracks. Performance of the algorithm is analysed based on experiments, which shows appealing results.
Zhan Wang & Dissanayake, G 1970, 'Efficient Monocular SLAM using sparse information filters', 2010 Fifth International Conference on Information and Automation for Sustainability, 2010 5th International Conference on Information and Automation for Sustainability (ICIAfS), IEEE, Colombo, Sri Lanka, pp. 311-316.
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A new method for efficiently mapping three dimensional environments from a platform carrying a single calibrated camera, and simultaneously localizing the platform within this map is presented in this paper. This is the Monocular SLAM problem in robotics, which is equivalent to the problem of extracting Structure from Motion (SFM) in computer vision. A novel formulation of Monocular SLAM which exploits recent results from multi-view geometry to partition the feature location measurements extracted from images into providing estimates of environment representation and platform motion is developed. Proposed formulation allows rich geometric information from a large set of features extracted from images to be maximally incorporated during the estimation process, without a corresponding increase in the computational cost, resulting in more accurate estimates. A sparse Extended Information Filter (EIF) which fully exploits the sparse structure of the problem is used to generate camera pose and feature location estimates. Experimental results are provided to verify the algorithm.
Zhao, L, Huang, S, Yan, L, Wang, JJ, Hu, G, Dissanayake, G & IEEE 1970, 'Large-Scale Monocular SLAM by Local Bundle Adjustment and Map Joining', 11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), IEEE, Singapore, pp. 431-436.
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This paper first demonstrates an interesting property of bundle adjustment (BA), 'scale drift correction'. Here 'scale drift correction' means that BA can converge to the correct solution (up to a scale) even if the initial values of the camera pose translations and point feature positions are calculated using very different scale factors. This property together with other properties of BA makes it the best approach for monocular Simultaneous Localization and Mapping (SLAM), without considering the computational complexity. This naturally leads to the idea of using local BA and map joining to solve large-scale monocular SLAM problem, which is proposed in this paper. The local maps are built through Scale-Invariant Feature Transform (SIFT) for feature detection and matching, random sample consensus (RANSAC) paradigm at different levels for robust outlier removal, and BA for optimization. To reduce the computational cost of the large-scale map building, the features in each local map are judiciously selected and then the local maps are combined using a recently developed 3D map joining algorithm. The proposed large-scale monocular SLAM algorithm is evaluated using a publicly available dataset with centimeter-level ground truth. ©2010 IEEE.
