Bowmer, KH 2014, 'Water resources in Australia: deliberation on options for protection and management', Australasian Journal of Environmental Management, vol. 21, no. 3, pp. 228-240.
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This paper first explains what is meant by wicked problems. Second it will compare Horst Rittel's reasons for identifying wicked problems as opposed to tame problems and Peter Checkland's development of soft systems thinking as opposed to hard systems thinking. it will then describe the process used at the National ALARA conference workshop held at Canberra and the outcome of the exploration. The paper will end with some reflections on the use of SSM to deal with wicked problems.
Cai, B, Huang, S, Liu, D & Dissanayake, G 2014, 'Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals', ROBOTICS AND AUTONOMOUS SYSTEMS, vol. 62, no. 4, pp. 506-514.
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This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies-Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy-are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms-an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)-to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals. © 2013 Elsevier B.V. All rights reserved.
Fitch, R, Stoy, K, Kernbach, S, Nagpal, R & Shen, W-M 2014, 'Reconfigurable modular robotics', ROBOTICS AND AUTONOMOUS SYSTEMS, vol. 62, no. 7, pp. 943-944.
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Gajendran, T, Brewer, G, Gudergan, S & Sankaran, S 2014, 'Deconstructing dynamic capabilities: the role of cognitive and organizational routines in the innovation process', Construction Management and Economics, vol. 32, no. 3, pp. 246-261.
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A better understanding of innovation processes might lead to productivity improvements. By focusing on a specific, economically relevant sector (construction) and on a specific type of firm (small to medium-sized enterprises, SMEs), the dynamic capabilities framework is extended by clarifying the roles of cognitive and organizational routines in organizational innovation processes. Insights generated from an in-depth case study of a medium-sized construction firm reveal that dynamic capabilities might diminish the relevance of an explicit innovation focus, because such capabilities have the potential to trigger emergent, incremental innovations. Accordingly, for construction SMEs, a development, rather than research, mode of innovation appears relatively more critical, as manifested in conscious cognitive routines and functional/integrative organizational routines.
Gan, SK, Fitch, R & Sukkarieh, S 2014, 'Online decentralized information gathering with spatial-temporal constraints', AUTONOMOUS ROBOTS, vol. 37, no. 1, pp. 1-25.
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We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial-temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents' trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial-temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources. © 2014 Springer Science+Business Media New York.
Gray, J, Williams, J, Hagare, P, Lopes, A & Sankaran, S 2014, 'Lessons Learnt from Educating University Students through a Trans-Disciplinary Project for Sustainable Sanitation Using a Systems Approach and Problem-Based Learning', Systems, vol. 2, no. 3, pp. 243-272.
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This article discusses how a Systems Thinking (ST) approach to student learning, employing Problem-Based Learning (PBL) interventions, at several different universities in Sydney, Australia was incorporated into a broader trans-disciplinary research project, the aim of which was to examine how urine diversion in an urban, institutional setting might form the basis of phosphorus collection—phosphorus being a non-renewable resource used in agricultural fertilizers. The article explores how the ST approach employed by the researchers themselves was adapted to embrace student engagement opportunities and how it permitted opportunities for Problem-Based Learning interventions. Five academics forming part of the research team consider the effectiveness of ST-styled student engagement via Problem-Based Learning in three action research cycles used in the research project. In sharing their experiences they provide an honest, “no-holds barred” review of what worked and what could be done more effectively with the benefits of hindsight.
Khushaba, RN, Takruri, M, Miro, JV & Kodagoda, S 2014, 'Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features', NEURAL NETWORKS, vol. 55, pp. 42-58.
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Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made a...
Kodagoda, S & Sehestedt, S 2014, 'Simultaneous people tracking and motion pattern learning', Expert Systems with Applications, vol. 41, no. 16, pp. 7272-7280.
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The field of Human Robot Interaction (HRI) encompasses many difficult challenges as robots need a better understanding of human actions. Human detection and tracking play a major role in such scenarios. One of the main challenges is to track them with long term occlusions due to agile nature of human navigation. However, in general humans do not make random movements. They tend to follow common motion patterns depending on their intentions and environmental/physical constraints. Therefore, knowledge of such common motion patterns could allow a robotic device to robustly track people even with long term occlusions. On the other hand, once a robust tracking is achieved, they can be used to enhance common motion pattern models allowing robots to adapt to new motion patterns that could appear in the environment. Therefore, this paper proposes to learn human motion patterns based on Sampled Hidden Markov Model (SHMM) and simultaneously track people using a particle filter tracker. The proposed simultaneous people tracking and human motion pattern learning has not only improved the tracking robustness compared to more conservative approaches, it has also proven robustness to prolonged occlusions and maintaining identity. Furthermore, the integration of people tracking and on-line SHMM learning have led to improved learning performance. These claims are supported by real world experiments carried out on a robot with suite of sensors including a laser range finder.
Kuo, V & Fitch, R 2014, 'Scalable multi-radio communication in modular robots', ROBOTICS AND AUTONOMOUS SYSTEMS, vol. 62, no. 7, pp. 1034-1046.
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Decentralized control of self-reconfiguring modular robots requires reliable inter-module communication. Communication links must tolerate module misalignment and implement the neighbor-to-neighbor communication model. In this paper, we propose a wireless system based on multiple radios per module that addresses these challenges. Although the capacity of general wireless mesh networks is known to rapidly decrease with network size, we show that a multi-radio single-channel system has constant capacity in square and cubic lattices of infinite size. We validate the performance of such a system in a testbed with 15 unactuated modules using synthetic data and a benchmark decentralized algorithm. We also demonstrate automatic neighbor detection. The main benefits of radio communication in modular robots are tolerance to module misalignment and to eliminate the tight coupling between communication and mechanical design necessitated by typical existing infrared and wired systems. Our results are the first to establish the feasibility of radio as the primary means of inter-module communication in modular robots. © 2013 Elsevier B.V. All rights reserved.
Liu, H, Li, D, Kim, J & Zhong, Y 2014, 'Real-Time Implementation of Decoupled Controllers for Multirotor Aircrafts', Journal of Intelligent & Robotic Systems, vol. 73, no. 1-4, pp. 197-207.
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Peynot, T, Lui, S-T, McAllister, R, Fitch, R & Sukkarieh, S 2014, 'Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain', JOURNAL OF FIELD ROBOTICS, vol. 31, no. 6, pp. 969-995.
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© 2014 Wiley Periodicals, Inc. Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion-planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and it predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.
Pitsis, TS, Sankaran, S, Gudergan, S & Clegg, SR 2014, 'Governing projects under complexity: theory and practice in project management', INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT, vol. 32, no. 8, pp. 1285-1290.
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© 2014. In this paper we argue that the fledgling field of project and program governance has the potential to make a major scholarly and practical contribution. One that not only has the potential to mainstream project management within the broader business and management field, but to also cement its place as a dominant voice in the successful governance of the strategic intentions of organizations, societies, and nations. With this argument in mind three themes organize present discussion in this issue of International Journal of Project Management: the first concerns how we should make sense of governance, something that is clarified through a review of the current state of play in the literature; the second theme comprises papers that report research conducted on governance in projects, using insights from surveys, case studies and other systematic forms of empirical observation. The third theme focuses on theoretical models of governance, ranging from distributed knowledge management and learning perspectives on project governance to systems engineering approaches. While we do not claim that this issue is exhaustive, we do believe it provides a sign post about the current state of play, and the potential future of governance in project and program management as a mainstream domain of research, theory and practice.
Sankaran, S 2014, 'Editorial', ORGANISATIONAL PROJECT MANAGEMENT, vol. 1, no. 1.
Sankaran, S, Okay, N & Chroust, G 2014, 'Special issue', INTERNATIONAL JOURNAL OF MANAGING PROJECTS IN BUSINESS, vol. 7, no. 3.
Sun, Y, Zhao, L, Huang, S, Yan, L & Dissanayake, G 2014, 'L-2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry', ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, vol. 91, pp. 1-16.
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The primary contribution of this paper is an efficient feature extraction and matching implementation for large images in large-scale aerial photogrammetry experiments. First, a Block-SIFT method is designed to overcome the memory limitation of SIFT for extracting and matching features from large photogrammetric images. For each pair of images, the original large image is split into blocks and the possible corresponding blocks in the other image are determined by pre-estimating the relative transformation between the two images. Because of the reduced memory requirement, features can be extracted and matched from the original images without down-sampling. Next, a red-black tree data structure is applied to create a feature relationship to reduce the search complexity when matching tie points. Meanwhile, tree key exchange and segment matching methods are proposed to match the tie points along-track and across-track. Finally, to evaluate the accuracy of the features extracted and matched from the proposed L2-SIFT algorithm, a bundle adjustment with parallax angle feature parametrization (ParallaxBA1The ParallaxBA source code is available open-source at OpenSLAM http://openslam.org/ParallaxBA.html. The features of the test datasets in this paper as the input to ParallaxBA are also available at OpenSLAM.1) is applied to obtain the Mean Square Error (MSE) of the feature reprojections, where the feature extraction and matching result is the only information used in the nonlinear optimisation system. Seven different experimental aerial photogrammetric datasets are used to demonstrate the efficiency and validity of the proposed algorithm. It is demonstrated that more than 33 million features can be extracted and matched from the Taian dataset with 737 images within 21h using the L2-SIFT algorithm. In addition, the ParallaxBA involving more than 2.7 million features and 6 million image points can easily converge to an MSE of 0.03874. The C/C++ source code for th...
To, AW-K, Paul, G & Liu, D 2014, 'Surface-Type Classification Using RGB-D.', IEEE Trans Autom. Sci. Eng., vol. 11, no. 2, pp. 359-366.
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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 Lcolor 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 Lab 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 i...
Zhou, J, Zhang, Q, Men, B & Huang, S 2014, 'Input‐to‐state stability of a class of descriptor systems', International Journal of Robust and Nonlinear Control, vol. 24, no. 1, pp. 97-109.
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SUMMARYThis paper studies the input‐to‐state stability (ISS) of descriptor systems with exogenous disturbances. on the basis of the ISS theory of standard state‐space nonlinear systems, a sufficient condition for a class of nonlinear descriptor system to be ISS is proved. Furthermore, a design method of the state feedback controllers is given to make the closed‐loop system ISS. A numerical example is given to illustrate the effectiveness of the controller design.Copyright © 2012 John Wiley & Sons, Ltd.
Abeywardena, D, Wang, Z, Dissanayake, G, Waslander, SL, Kodagoda, S & IEEE 1970, 'Model-aided State Estimation for Quadrotor Micro Air Vehicles amidst Wind Disturbances', 2014 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2014), IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Chicago, Illinois, pp. 4813-4818.
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© 2014 IEEE. This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims.
Andonovski, B, Miro, JV, Poon, J & Black, R 1970, 'An automated mechanism to characterize wheelchair user performance', 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE, Sau Paulo, Brazil, pp. 444-449.
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This paper proposes a mechanism to derive quantitative descriptions of wheelchair usage as a tool to aid Occupational Therapist with their performance assesment of mobility platform users. This is accomplished by analysing data computed from a standalone sensor package fitted on an wheelchair platform. This work builds upon previous propositions where parameters that could assist in the assessment were recommended to the authors by a qualified occupational therapist (OT). In the current scheme however the task-specific parameters that may provide the most relevant user information for the assessment are automatically revealed through a machine learning approach. Data mining techniques are used to reveal the most informative parameters, and results from three typical classifiers are presented based on learnings from manual labelling of the training data. Trials conducted by healthy volunteers gave classifications with an 81% success rate using a Random Forest classifier, a promising outcome that sets the scene for a potential clinical trial with a larger user pool.
Barnes, B, Abeywardena, D, Kodagoda, S & Dissanayake, G 1970, 'Evaluation of feature detectors for KLT based feature tracking using the odroid U3', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Melbourne.
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Feature tracking is an integral part of most vision-based state estimation frameworks. However, tracking features at a sufficient frame rate is a challenging task for mobile robots such as Micro Aerial Vehicles (MAVs) due to their fast dynamics and limited on-board computing resources. Recent developments in smartphone processors have led to embedded computing platforms that are ideal on-board computers for MAV state estimation. This paper analyses the performance of a Kanade-Lucas-Thomasi (KLT) based feature tracker on a state-of-theart embedded computing platform suitable for on-board MAV state estimation. It compares the performance of different implementations of the feature tracker using four different lowcomplexity feature detectors. The experimental results presented herein may serve as guidelines for the selection of a feature detector, image resolution, framerate and feature quantity when developing on-board feature tracking systems based on ARM Cortex-A9 embedded computers.
Best, G & Anstee, S 1970, 'Motion planning for autonomous underwater vehicle supervision', Australasian Conference on Robotics and Automation, ACRA.
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Autonomous Underwater Vehicles (AUV) play an important role in surveying undersea environments. It is often essential for AUV operators to be able to supervise the progress of their vehicles during missions, to enable informed decisions in real-time. In this paper we consider supervision of an AUV executing a preplanned mission from a mobile surface vessel equipped with an acoustic communications system. We consider the surface vessel to be supervising effectively if it is stationary and located within a fixed maximum range from the AUV. When the supervising vessel is not stationary, we assume it is transiting to the next supervision point. We seek trajectory plans that maximise the proportion of time the supervising vessel is able to supervise the AUV effectively. In this paper we propose two solutions to this trajectory planning problem; a greedy planner, which instructs the supervising vessel to move when the AUV moves beyond its effective range but restricts the vessel to points along the path of the AUV; and a global optimiser based on a genetic algorithm that relaxes this limitation. Sea-trial and simulation results demonstrate the effectiveness of our proposed solutions.
Best, G & Moghadam, P 1970, 'An evaluation of multi-modal user interface elements for tablet-based robot teleoperation', Australasian Conference on Robotics and Automation, ACRA.
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For robot teleoperation systems, tablet and smart phone user interfaces provide portability and accessibility to allow anyone anywhere to connect to the system quickly and easily. This can be highly advantageous for disaster-relief robot systems where timing is critical. However, the small screen size and unconventional input methods mean that traditional teleoperation user interface elements, such as multiple visual windows, keyboards and mice, are no longer effective. In this paper we propose and investigate multi-modal user interface design principles as a solution for effective tablet-based teleoperation. We present our tablet user interface that features multiple complementary modalities of control and feedback mechanisms. It allows the operator to perceive the robot's environment and state by exciting their senses of vision, hearing and touch with overlaying visual displays, virtual 3-D audio and vibration feedback. The operator commands actions to the robot through the use of intuitive input methods utilising the touch screen and accelerometer. We analyse these design principles by performing a user study that focuses on finding what aspects of the multi-modal user interface affect the system performance during a navigation task. The study highlights advantages of multimodal user interfaces for robot teleoperation and makes several findings to be considered when developing similar systems.
Carmichael, MG, Moutrie, B & Liu, D 1970, 'A framework for task-based evaluation of robotic coworkers', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Singapore, pp. 1362-1367.
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© 2014 IEEE. Compared to a robotic system that performs a task alone, a robot coworker performing tasks in collaboration with a human operator is subject to additional constraints which can limit the ability of the system to perform the task as required. This work presents a framework for analyzing the ability of a robotic coworker to perform specific tasks in collaboration with a human. The framework allows systematic evaluation of robotic systems based on traditional robot performance measures such as reachable workspace and payload capacity, as well as considering additional factors which arise due to the task being performed collaboratively with a human; such as the reach and strength of the human, human-robot collision, and satisfying desired assistance paradigms. Application of the framework is demonstrated in a case study analyzing a robot designed to assist a human during a materials handling task.
Cheng, J & Kim, J 1970, 'Delayed Optimisation for Pose-Graph SLAM', Australasian Conference on Robotics and Automation, Melbourne.
Cheng, J, Jiang, Z, Zhang, Y & Kim, J 1970, 'Toward robust linear SLAM', 2014 IEEE International Conference on Mechatronics and Automation, 2014 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE, IEEE, pp. 705-710.
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Cheng, J, Kim, J, Jiang, Z & Yang, X 1970, 'Compressed Unscented Kalman filter-based SLAM', 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE, IEEE, pp. 1602-1607.
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Cheng, J, Kim, J, Jiang, Z & Zhang, W 1970, 'Tightly Coupled SLAM/GNSS for Land Vehicle Navigation', Lecture Notes in Electrical Engineering, Springer Berlin Heidelberg, pp. 721-733.
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Simultaneous Localization and Mapping (SLAM) algorithm takes the advantages of online map building without any prior environment information and simultaneously location determining with the generated map. This paper proposes an innovative navigation algorithm, tightly coupling of SLAM and GNSS. If GNSS signals are available, the GNSS raw measurements are fused with SLAM measurements to correct the errors of the system's pose as well as reducing the uncertainty of the map. In the GNSS-denied environments, the system operates at the stand-alone SLAM to provide continuous navigation solutions. Considering the computational cost problem, Compressed Extended Kalman Filter (CEKF) is employed to the multi-sensor data fusion. The simulation of the proposed algorithm is implemented in the simulated large-scale environment. Results demonstrate that the proposed technique provides a high accuracy of trajectory tracking in complex environments, and improves greatly the performance of data association and loop-closure detection. © 2014 Springer-Verlag.
Dantanarayana, L, Ranasinghe, R, Tran, A, Liu, D & Dissanayake, G 1970, 'A Novel Collaboratively Designed Robot to Assist Carers', SOCIAL ROBOTICS, International Conference on Social Robotics (ICSR), SPRINGER-VERLAG BERLIN, Sydney, Australia, pp. 105-114.
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© Springer International Publishing Switzerland 2014. This paper presents a co-design process and an assisted navigation strategy that enables a novel assistive robot, Smart Hoist, to aid carers transferring non-ambulatory residents. Smart Hoist was codesigned with residents and carers at IRT Woonona residential care facility to ensure that the device can coexist in the facility, while providing assistance to carers with the primary aim of reducing lower back injuries, and improving the safety of carers and patients during transfers.The Smart Hoist is equipped with simple interfaces to capture user intention in order to provide assisted manoeuvring. Using the RGB-D sensor attached to the device, we propose a method of generating a repulsive force that can be combined with the motion controller’s output to allow for intuitive manoeuvring of the Smart Hoist, while negotiating with the environment.Extensive user trials were conducted on the premises of IRTWoonona residential care facility and feedback from end users confirm its intended purpose of intuitive behaviour, improved performance and ease of use.
Falque, R, Vidal-Calleja, T, Miro, JV, Lingnau, DC & Russell, DE 1970, 'Background segmentation to enhance remote field eddy current signals', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Melbourne University.
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Pipe condition assessment is critical to avoid breakages. Remote Field Eddy Current (RFEC) is a commonly used technology to assess the condition of pipes. The nature of this technology induces some particular noise into its measurements. In this paper, we develop a 3D simulation based on the Finite Element Analysis to study the properties of this noise. Moreover, we propose a filtering process based on a modified version of graph-cuts segmentation method to remove the influence of this noise. Simulated data together with an experimental data-set obtained from a real RFEC inspection show the validity of the proposed approach.
Gandhi, S, Sankaran, S, Er, M, Orr, K & Khabbaz, H 1970, 'Developing Technology-Assisted Multi-Disciplinary Learning Strategies', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 31st International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Sydney, pp. 346-353.
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The construction industry is multi-disciplinary and collaborative in nature. Project managers are expected to understand the relations, roles and responsibilities in this collaborative working environment. Construction project managers need to be equipped with skills to process and understand the principles of interdisciplinary working. In order to keep pace with industry requirements, it is necessary for universities to teach subjects in tertiary education courses that educate and motivate construction students towards interdisciplinary working. This paper is based on a research project aimed at understanding opportunities and challenges for introducing subjects that require students from different disciplines to work together on an integrated project. In order to teach interdisciplinary working principles to project management students, a new post-graduate subject, Integrated Project Delivery, was introduced in the Master of Project Management at the University of Technology Sydney (UTS) as part of a construction sub-major. The subject was designed and teaching materials prepared based on inputs from architecture, engineering and construction management academics. In the teaching of the subject, interdisciplinary student teams were formed based on educational background and professional experience. An (pedagogical) action research approach was adopted to study the challenges and benefits of new ways of learning in line with the UTS learning strategies being adopted by academics. The main finding of this research concluded that project-based learning is very valuable to both students and industry alike as it promotes working on a live project enthusiastically and gaining industry experience in new ways of working adopted by industry. Working in multi-disciplinary teams requires students to respect the other team participants from different backgrounds, inculcating values of team spirit and discouraging adversarial behaviours. Furthermore this research ...
Gerardo-Castro, MP, Peynot, T, Ramos, F, Fitch, R & IEEE 1970, 'Robust Multiple-Sensing-Modality Data Fusion using Gaussian Process Implicit Surfaces', 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), International Conference on Information Fusion, IEEE, Salamanca, Spain.
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© 2014 International Society of Information Fusion. The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.
Hassan, M, Liu, D, Huang, S & Dissanayake, G 1970, 'Task oriented area partitioning and allocation for optimal operation of multiple industrial robots in unstructured environments', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Marina Bay Sands, Singapore, pp. 1184-1189.
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© 2014 IEEE. When multiple industrial robots are deployed in field applications such as grit blasting and spray painting of steel bridges, the environments are unstructured for robot operation and the robot positions may not be arranged accurately. Coordination of these multiple robots to maximize productivity through area partitioning and allocation is crucial. This paper presents a novel approach to area partitioning and allocation by utilizing multiobjective optimization and voronoi partitioning. Multiobjective optimization is used to minimize: (1) completion time, (2) proximity of the allocated area to the robot, and (3) the torque experienced by each joint of the robot during task execution. Seed points of the voronoi graph for voronoi partitioning are designed to be the design variables of the multiobjective optimization algorithm. Results of three different simulation scenarios are presented to demonstrate the effectiveness of the proposed approach and the advantage of incorporating robots' torque capacity.
Jadidi, MG, Miro, JV, Valencia, R & Andrade-Cetto, J 1970, 'Exploration on continuous Gaussian process frontier maps', 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, pp. 6077-6082.
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© 2014 IEEE. An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in This paper. The approach conveniently handles sparse sensor measurements To build a continuous model of The environment That exploits structural dependencies without The need To resort To a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of The proposed approach is evaluated Through simulation results in The well-known Freiburg and Cave maps.
Khosoussi, K, Huang, S & Dissanayake, G 1970, 'Novel insights into the impact of graph structure on SLAM', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, Chicago, IL, pp. 2707-2714.
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© 2014 IEEE. SLAM can be viewed as an estimation problem over graphs. It is well known that the topology of each dataset affects the quality of the corresponding optimal estimate. In this paper we present a formal analysis of the impact of graph structure on the reliability of the maximum likelihood estimator. In particular, we show that the number of spanning trees in the graph is closely related to the D-optimality criterion in experimental design. We also reveal that in a special class of linear-Gaussian estimation problems over graphs, the algebraic connectivity is related to the E-optimality design criterion. Furthermore, we explain how the average node degree of the graph is related to the ratio between the minimum of negative log-likelihood achievable and its value at the ground truth. These novel insights give us a deeper understanding of the SLAM problem. Finally we discuss two important applications of our analysis in active measurement selection and graph pruning. The results obtained from simulations and experiments on real data confirm our theoretical findings.
Kim, J & Cheng, J 1970, 'Delayed optimisation for robust and linear pose-graph SLAM', Australasian Conference on Robotics and Automation, ACRA.
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This paper addresses a robust and efficient solution to eliminate false loop-closures in a posegraph linear SLAM problem. Linear SLAM was recently demonstrated based on submap joining techniques in which a nonlinear coordinate transformation was performed separately out of the optimisation loop, resulting in a convex optimisation problem. This however introduces added complexity in dealing with any false loop-closures, which mostly stems from two factors: a) the limited local observations in submap-joining stages and b) the non blockdiagonal nature of the information matrix of each submap. To address these problems, we propose a Robust Linear SLAM (RL-SLAM) by 1) developing a delayed optimisation for outlier candidates and 2) utilising a Schur complement to efficiently eliminate corrupted information block. Based on this new strategy, we prove that the spread of outlier information does not compromise the optimisation performance of inliers and can be fully filtered out from the corrupted information matrix. Experimental results based on public synthetic and real-world datasets in 2D and 3D environments show that this robust approach can cope with the incorrect loop-closures robustly and effectively.
Kim, S & Kim, J 1970, 'Recursive Bayesian updates for occupancy mapping and surface reconstruction', Australasian Conference on Robotics and Automation, ACRA.
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This paper proposes a new method to build two kinds of map representations, occupancy maps and surface meshes, in a single framework of Gaussian processes and update recursively using Bayesian Committee Machines. Previously, Gaussian processes were applied to robotic mapping as a batch process considering all the observations at once. However, that approach not only increases the number of training data, which is critical to the time complexity of Gaussian processes, but also is not able to update the final map with new observations. Therefore, we propose to recursively update Gaussian process maps using Bayesian Committee Machines based on the static world assumption. We demonstrate our method with a real dataset and compare the accuracy and run time with OctoMaps. Experimental results confirm that our method successfully works with a sequence of observations. Our method is slower than OctoMaps but generates more accurate occupancy maps as well as surface meshes without additional cost of computation.
Kirchner, N, Alempijevic, A, Virgona, A, Dai, X, Plöger, PG & Venkat, RK 1970, 'A robust people detection, tracking, and counting system', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, Australasian Robotics and Automation Association, Melbourne, Australia, pp. 1-8.
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The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed. This paper presents our approach for robust real world people detection, tracking and counting using a PrimeSense RGBD camera. Our past research, upon which we built, is highlighted and novel methods to solve the problems of sensor self-localisation, false negatives due to persons physically interacting with the environment, and track misassociation due to crowdedness are presented. An empirical evaluation of our approach in a major Sydney public train station (N=420) was conducted, and results demonstrating our methods in the complexities of this challenging environment are presented.
Lee, JJH, Frey, K, Fitch, R & Sukkarieh, S 1970, 'Fast path planning for precision weeding', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Melbourne, Australia.
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Agricultural robots have the potential to reduce herbicide use in agriculture and horticulture through autonomous precision weeding. One of the main challenges is how to efficiently plan paths for a robot arm such that many individual weeds can be processed quickly. This paper considers an abstract weeding task among obstacles and proposes an efficient online path planning algorithm for an industrial manipulator mounted to a mobile robot chassis. The algorithm is based on a multi-query approach, inspired by industrial bin-picking, where a database of high-quality paths is computed offline and paths are then selected and adapted online. We present a preliminary implementation using a 6-DOF arm and report results from simulation experiments designed to evaluate system performance with varying database and obstacle sizes. We also validate the approach using a Universal Robots UR5 manipulator and ROS interface.
Liu, DK, Dissanayake, G, Valls Miro, J & Waldron, KJ 1970, 'Infrastructure Robotics: Research Challenges and Opportunities', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 31st International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Sydney, Australia, pp. 43-49.
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Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney.
Martín, F, Valls Miró, J & Moreno, L 1970, 'Towards Exploiting the Advantages of Colour in Scan Matching', Advances in Intelligent Systems and Computing, Iberian Robotics Conference, Springer International Publishing, Madrid, pp. 217-231.
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© Springer International Publishing Switzerland 2014. Colour plays an important role in the perception systems of the human beings. In robotics, the development of new sensors has made it possible to obtain colour information together with depth information about the environment. The exploitation of this type of information has become more and more important in numerous tasks. In our recent work, we have developed an evolutionary-based scan matching method. The aim of this work is to modify this method by the introduction of colour properties, taking the first steps in studying how to use colour to improve the scan matching. In particular, we have applied a colour transition detection method based on the delta E divergence between neighbours in a scan. Our algorithm has been tested in a real environment and significant conclusions have been reached.
Nguyen, LV, Kodagoda, S, Ranasinghe, R & Dissanayake, G 1970, 'Mobile robotic wireless sensor networks for efficient spatial prediction', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 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.
Norouzi, M, Miro, JV, Dissanayake, G & Vidal-Calleja, T 1970, 'Path planning with stability uncertainty for articulated mobile vehicles in challenging environments', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, Chicago, IL, pp. 1748-1753.
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© 2014 IEEE. This article proposes a probabilistic approach to account for robot stability uncertainty when planing motions over uneven terrains. A novel probabilistic stability criterion derived from the cumulative distribution of a tip-over metric is introduced that allows a safety constraint to be dynamically updated by available sensor data as it becomes available. The proposed safety constraint authorizes the planner to generates more conservative motion plans for areas with higher levels of uncertainty, while avoids unnecessary caution in well-known areas. The proposed systematic approach is particularly applicable to reconfigurable robots that can assume safer postures when required, although is equally valid for fixed-configuration platforms to choose safer paths to follow. The advantages of planning with the proposed probabilistic stability metric are demonstrated with data collected from an indoor rescue arena, as well as an outdoor rover testing facility.
Patel, M, Miro, JV & Dissanayake, G 1970, 'A probabilistic approach to learn activities of daily living of a mobility aid device user', 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, pp. 969-974.
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© 2014 IEEE. The problem of inferring human behaviour is naturally complex: people interact with the environment and each other in many different ways, and dealing with the often incomplete and uncertain sensed data by which the actions are perceived only compounds the difficulty of the problem. In this paper, we propose a framework whereby these elaborate behaviours can be naturally simplified by decomposing them into smaller activities, whose temporal dependencies can be more efficiently represented via probabilistic hierarchical learning models. In this regard, patterns of a number of activities typically carried out by users of an ambulatory aid device have been identified with the aid of a Hierarchical Hidden Markov Model (HHMM) framework. By decomposing the complex behaviours into multiple layers of abstraction the approach is shown capable of modelling and learning these tightly coupled human-machine interactions. The inference accuracy of the proposed model is proven to compare favourably against more traditional discriminative models, as well as other compatible generative strategies to provide a complete picture that highlights the benefits of the proposed approach, and opens the door to more intelligent assistance with a robotic mobility aid.
Piyathilaka, L & Kodagoda, S 1970, 'Active visual object search using affordance-map in real world: A human-centric approach.', ICARCV, International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 1427-1432.
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Human context is the most natural explanation why objects are placed and arranged in a particular order in an indoor environment. Usually, humans arrange objects in order to support their intended activities in a given environment. However, most of the common approaches for robotic object search involve modelling object-object relationships. In this paper, we hypothesize such relationships are centered around humans and bring human context to object search by modelling human-objects
relationships through affordance-map. It identifies locations in a 3D map which support a particular affordance using virtual human models. Therefore, our approach does not require to observe real humans in the scene. The affordance-map and object-human-robot relationship are then used to infer the object search
strategy. We tested our algorithm using a mobile robot that actively searched for the object “computer monitors” in an office environment with promising results
Piyathilaka, L & Kodagoda, S 1970, 'Affordance-map: A map for context-aware path planning', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, Australian Robotics and Automation Association Inc, Melbourne, pp. 1-8.
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'Context-awareness' could be one of the most desired fundamental abilities that a robot should have when sharing a workspace with humans co-workers. Arguably, a robot with appropriate context-awareness could lead to a better human robot interaction. In this paper, we address the problem of combining contextawareness with robotic path planning. Our approach is based on affordance-map, which involves mapping latent human actions in a given environment by looking at geometric features of the environment. This enables us to learn human context in an given environment without observing real human behaviours which themselves are a non-trivial task to detect. Once learned, affordance-map allows us to assign an affordance cost value for each grid location of the map. These cost maps are later used to develop a context-aware global path planning strategy by using the well known A∗ algorithm. The proposed method was tested in a real office environment and proved our algorithm is capable of moving a robot in a path that minimises the distractions to human co-workers.
Poon, J & Miro, JV 1970, 'A Multi-modal Utility to Assist Powered Mobility Device Navigation Tasks', SOCIAL ROBOTICS, International Conference on Social Robotics (ICSR), Springer International Publishing, Sydney, Australia, pp. 300-309.
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© Springer International Publishing Switzerland 2014. This paper presents the development of a shared control systemfor power mobility device users of varying capability in order toreduce carer oversight in navigation. Weighting of a user’s joystick inputagainst a short-tem trajectory prediction and obstacle avoidancealgorithm is conducted by taking into consideration proximity to obstaclesand smoothness of user driving, resulting in capable users rewardedgreater levels of manual control for undertaking maneuvres that can beconsidered more challenging. An additional optional comparison with aVector Field Histogram applied to leader-tracking provides further activities,such as completely autonomous following and a task for the userto follow a leading entity. Indoor tests carried out on university campusdemonstrate the viability of this work, with future trials at a care homefor the disabled intended to show the system functioning in one of itsintended settings.
Qayyum, U & Kim, J 1970, 'Global Optimization for 2D SLAM Problem', Proceedings of Informatics in Control, Automation and Robotics, Springer International Publishing, pp. 35-49.
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Quin, P, Alempijevic, A, Paul, G & Liu, D 1970, 'Expanding wavefront frontier detection: An approach for efficiently detecting frontier cells', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, Australasian Robotics and Automation Association, Melbourne, pp. 1-10.
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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.
Skinner, B, Vidal-Calleja, T, Miro, JV, De Bruijn, F & Falque, R 1970, '3D point cloud upsampling for accurate reconstruction of dense 2.5D thickness maps', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Melbourne University.
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This paper presents a novel robust processing methodology for computing 2.5D thickness maps from dense 3D collocated surfaces. The proposed pipeline is suitable to faithfully adjust data representation detailing as required, from preserving fine surface features to coarse interpretations. The foundations of the proposed technique exploit spatial point-based filtering, ray tracing techniques and the Robust Implicit Moving Least Squares (RIMLS) algorithm applied to dense 3D datasets, such as those acquired from laser scanners. The effectiveness of the proposed technique in overcoming traditional angular aliasing and corruption artifacts is validated with 3D ranging data acquired from internal and external surfaces of exhumed water pipes. It is shown that the resulting 2.5D maps can be more accurately and completely computed to higher resolutions, while significantly reducing the number of raytracing errors when compared with 2.5D thickness maps derived from our current approach.
Su, D & Miro, JV 1970, 'An ultrasonic/RF GP-based sensor model robotic solution for indoors/outdoors person tracking', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Singapore, Singapore, pp. 1662-1667.
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© 2014 IEEE. An non-linear Bayesian regression engine for robotic tracking based on an ultrasonic/RF sensor unit is presented in this paper. The proposed system is able to maintain systematic tracking of a leading human in indoor/outdoor settings with minimalistic instrumentation. Compared to popular camera based localization system the sonar array/RF based system has the advantage of being insensitive to background light intensity changes, a primary concern in outdoor environments. In contrast to single-plane laser range finder based tracking the proposed scheme is able to better adapt to small terrain variations, while at the same time being a significantly more affordable proposition for tracking with a robotic unit. A key novelty in this work is the utilisation of Gaussian Process Regression (GPR) to build a model for the sensor unit, which is shown to compare favourably against traditional linear triangulation approaches. The covariance function yield by the GPR sensor model also provides the additional benefit of outlier rejection. We present experimental results of indoors and outdoors tracking by mounting the sensor unit on a Garden Utility Transportation System (GUTS) robot and compare the proposed approach with linear triangulation which clearly show the inference engine capability to generalise relative localisation of human and a marked improvement in tracking accuracy and robustness.
Ulapane, N, Alempijevic, A, Vidal-Calleja, T, Miro, JV, Rudd, J & Roubal, M 1970, 'Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling', 2014 9th IEEE Conference on Industrial Electronics and Applications, 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA), IEEE, Hangzhou, PEOPLES R CHINA, pp. 1762-1767.
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© 2014 IEEE. This paper describes a Gaussian Process based machine learning technique to estimate the remaining volume of cast iron in ageing water pipes. The method utilizes time domain signals produced by a commercially available pulsed Eddy current sensor. Data produced by the sensor are used to train a Gaussian Process model and perform inference of the remaining metal volume. The Gaussian Process model was learned using sensor data obtained from cast iron calibration plates of various thicknesses. Results produced by the Gaussian Process model were validated against the remaining wall thickness acquired using a high resolution laser scanner after the pipes were sandblasted to remove corrosion. The evaluation shows agreement between model outputs and ground truth. The paper concludes by discussing the implications or results and how the proposed method can potentially advance the current technological setup by facilitating real time pipe profiling.
Van Nguyen, L, Kodagoda, S, Ranasinghe, R & Dissanayake, G 1970, 'Spatially-distributed prediction with mobile robotic wireless sensor networks', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, 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.
Van Nguyen, L, Kodagoda, S, Ranasinghe, R, Dissanayake, G, Bustamante, H, Vitanage, D & Nguyen, T 1970, 'Spatial prediction of hydrogen sulfide in sewers with a modified Gaussian process combined mutual information', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Marina Bay Sands, Singapore, pp. 1130-1135.
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© 2014 IEEE. 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.
Vidal-Calleja, T, Miro, JV, Martin, F, Lingnau, DC & Russell, DE 1970, 'Automatic detection and verification of pipeline construction features with multi-modal data', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, Chicago, IL, USA, pp. 3116-3122.
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© 2014 IEEE. Assessment of the condition of underground pipelines is crucial to avoid breakages. Autonomous in-line inspection tools provided with Non-destructive Technology (NDT) sensors to assess large sections of the pipeline are commonly used for these purposes. An example of such sensors based on Eddy currents is the Remote Field Technology (RFT). A crucial step during in-line inspections is the detection of construction features, such as joints and elbows, to accurately locate and size specific defects within pipe sections. This step is often performed manually with the aid of visual data, which results in slow data processing. In this paper, we propose a generic framework to automate the detection and verification of these construction features using both NDT sensor data and visual images. Firstly, supervised learning is used to identify the construction features in the NDT sensor signals. Then, image processing is employed to verify the selection. Results are presented with data from a RFT tool, for which a specialised descriptor has been designed to characterise and classify its signal features. Furthermore, the construction feature is displayed in the image, once it is identified in the RFT data and detected in the visual data. A visual odometry algorithm has been implemented to locate the visual data with respect to the RFT data. About 800 meters of these multi-modal data are evaluated to test the validity of the proposed approach.
Vidal-Calleja, T, Su, D, De Bruijn, F & Miro, JV 1970, 'Learning spatial correlations for Bayesian fusion in pipe thickness mapping', 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, pp. 1-8.
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Wang, Y & Huang, S 1970, 'Motion segmentation based robust RGB-D SLAM', Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014 11th World Congress on Intelligent Control and Automation (WCICA), IEEE, Shenyang, China, pp. 3122-3127.
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© 2014 IEEE. A sparse feature-based motion segmentation algorithm for RGB-D data is proposed which offers us a unified way to handle outliers and dynamic scenarios. Together with the pose-graph SLAM framework, they constitute an effective and robust solution that enable us to do RGB-D SLAM in wide range of situations, although traditionally they have been divided into different categories and treated separately using different kinds of methods. Through comparisons with RANSAC using simulated data and testing with different benchmark RGB-D datasets against the state-of-the-art method in RGB-D SLAM, we show that our solution is efficient and effective in handling general static and dynamic scenarios, some of which have not be achieved before.
Wang, Y & Huang, S 1970, 'Towards dense moving object segmentation based robust dense RGB-D SLAM in dynamic scenarios', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Singapore, pp. 1841-1846.
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© 2014 IEEE. Based on the latest achievements in computer vision and RGB-D SLAM, a practical way for dense moving object segmentation and thus a new framework for robust dense RGB-D SLAM in challenging dynamic scenarios is put forward. As the state-of-the-art method in RGB-D SLAM, dense SLAM is very robust when there are motion blur or featureless regions, while most of those sparse feature-based methods could not handle them. However, it is very susceptible to dynamic elements in the scenarios. To enhance its robustness in dynamic scenarios, we propose to combine dense moving object segmentation with dense SLAM. Since the object segmentation results from the latest available algorithm in computer vision are not satisfactory, we propose some effective measures to improve upon them so that better results can be achieved. After dense segmentation of dynamic objects, dense SLAM can be employed to estimate the camera poses. Quantitative results from the available challenging benchmark dataset have proved the effectiveness of our method.
Ward, PK, Manamperi, P, Brooks, P, Mann, P, Kaluarachchi, W, Matkovic, L, Paul, G, Yang, C, Quin, P, Pagano, D, Liu, D, Waldron, K & Dissanayake, G 1970, 'Climbing Robot for Steel Bridge Inspection: Design Challenges', Proceedings for the Austroads Publications Online, Austroads Bridge Conference, ARRB Group, New South Wales, pp. 1-13.
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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.
Wu, K, Ranasinghe, R & Dissanayake, G 1970, 'A fast pipeline for textured object recognition in clutter using an RGB-D sensor', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 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.
Zhang, T, Huang, S & Liu, D 1970, 'Comparison of two strategies of path planning for underwater robot navigation under uncertainty', 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Singapore, pp. 901-906.
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© 2014 IEEE. This paper considers path planning for underwater robot in navigation tasks. The main challenge is how to deal with uncertainties in the underwater environment such as motion model error and sensing error. To overcome this challenge, two high level control methods have been presented and compared, which are based on the Model Predictive Control (MPC) strategy and the Partially Observable Markov Decision Process (POMDP) model, respectively. Navigation time, collision frequency, energy consumption and accuracy in localization are used as the assessment criteria for the two methods. It is shown that the MPC-based method is more efficient for our application scenarios while the POMDP-based method can provide more robust solutions.
Zhang, T, Huang, S & Liu, D 1970, 'Comparison of Two Strategies of Path Planning for Underwater Robot Navigation Under Uncertainty', 2014 13TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION (ICARCV), 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, Singapore, SINGAPORE, pp. 901-906.
Zhao, L, Huang, S & Dissanayake, G 1970, 'Linear MonoSLAM: A linear approach to large-scale monocular SLAM problems', 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, China, pp. 1517-1523.
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© 2014 IEEE. This paper presents a linear approach for solving monocular simultaneous localization and mapping (SLAM) problems. The algorithm first builds a sequence of small initial submaps and then joins these submaps together in a divide-and-conquer (D&C) manner. Each of the initial submap is built using three monocular images by bundle adjustment (BA), which is a simple nonlinear optimization problem. Each step in the D&C submap joining is solved by a linear least squares together with a coordinate and scale transformation. Since the only nonlinear part is in the building of the initial submaps, the algorithm makes it possible to solve large-scale monocular SLAM while avoiding issues associated with initialization, iteration, and local minima that are present in most of the nonlinear optimization based algorithms currently used for large-scale monocular SLAM. Experimental results based on publically available datasets are used to demonstrate that the proposed algorithms yields solutions that are very close to those obtained using global BA starting from good initial guess.
