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, 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.
Clements, D., Dugdale, T., Hunt, T., Fitch, R., Hung, C., Sukkarieh, S. & Xu, Z. 2014, 'Detection of alligator weed using an unmanned aerial vehicle', Plant Protection Quarterly, vol. 29, no. 3, pp. 84-89.
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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Gan, S.K., 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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Khushaba, R.N., Takruri, M., Valls Miro, J. & Kodagoda, S. 2014, 'Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.', Neural networks : the official journal of the International Neural Network Society, vol. 55, pp. 42-58.
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Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available a...
Kodagoda, S. & Sehestedt, S.A. 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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Li, Y. & Jack Wang, J. 2014, 'A pedestrian navigation system based on low cost IMU', Journal of Navigation, vol. 67, no. 6, pp. 929-949.
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© 2014 The Royal Institute of Navigation. For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU, the zero velocity update (ZUPT technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closed-loop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms.
Patel, M., Valls Miro, J., Kragic, D., Ek, C.H. & Dissanayake, G. 2014, 'Learning object, grasping and manipulation activities using hierarchical HMMs', Autonomous Robots, vol. 37, no. 3, pp. 317-331.
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This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques. © 2014 Springer Science+Business Media New York.
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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Sun, Y., Zhao, L., Huang, S., Yan, L. & Dissanayake, G. 2014, 'L2-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, eatures 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 (ParallaxBA) 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 the proposed algorithm is available at http://services.eng.uts.edu.au/~sdhuang/research.htm.
To, A.W., Paul, G. & Liu, D. 2014, 'Surface-type classification using RGB-D', IEEE Transactions on Automation Science and Engineering, vol. 11, no. 2, pp. 359-366.
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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 L*a*b* color spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and L*a*b* features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in...
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, pp. 97-109.
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This 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.
Abeywardena, D.M., Wang, Z., Dissanayake, G., Waslander, S.L. & Kodagoda 2014, 'Model-aided State Estimation for Quadrotor Micro Air Vehicles amidstWind Disturbances', Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems, EEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Chicago, Illinois, pp. 4813-4818.
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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., Valls Miro, J., Poon, J.T. & Black, R. 2014, 'An automated mechanism to characterize wheelchair user performance', Proceedings of 2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, 5th International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE, Sau Paulo, Brazil, pp. 444-449.
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This paper proposes a mechanism to derive quantitative descriptions of wheelchair usage as a tool to aid Occupational Therapist with their performance assesment of mobility platform users. This is accomplished by analysing data computed from a standalone sensor package fitted on an wheelchair platform. This work builds upon previous propositions where parameters that could assist in the assessment were recommended to the authors by a qualified occupational therapist (OT). In the current scheme however the task-specific parameters that may provide the most relevant user information for the assessment are automatically revealed through a machine learning approach. Data mining techniques are used to reveal the most informative parameters, and results from three typical classifiers are presented based on learnings from manual labelling of the training data. Trials conducted by healthy volunteers gave classifications with an 81% success rate using a Random Forest classifier, a promising outcome that sets the scene for a potential clinical trial with a larger user pool.
Barnes, B., Abeywardena, D.M., Kodagoda, S. & Dissanayake, G. 2014, 'Evaluation of Feature Detectors for KLT based Feature Tracking usingthe Odroid U3', Australian Conference on Robotics and Automation (ACRA 2014), Australian Robotics and Automation Association, 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-the-art embedded computing platform suitable for on-board MAV state estimation. It compares the performance of different implementations of the feature tracker using four different low-complexity 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.
Carmichael, M.G., Moutrie, B. & Liu, D. 2014, 'A Framework for Task-Based Evaluation of Robotic Coworkers', The 13th International Conference on Control, Automation, Robotics and Vision (ICARCV2014), IEEE, Singapore, pp. 1362-1367.
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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.
Dantanarayana, L., Ranasinghe, R., Tran, A., Liu, D. & Dissanayake, G. 2014, 'A Novel Collaboratively Designed Robot to Assist Carers', SOCIAL ROBOTICS, pp. 105-114.
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Falque, R., Vidal-Calleja, T., Valls Miro, J., Lingnau, D.C. & Russel, D.E. 2014, 'Background Segmentation to Enhance Remote Field Eddy Current Signals', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/at_a_glance.html, Australasian Conference on Robotics and Automation, Melbourne University.
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Pipe condition assessment is critical to avoid breakages. Remote Field Eddy Current (RFEC) is a commonly used technology to assess the condition of pipes. The nature of this technology induces some particular noise into its measurements. In this paper, we develop a 3D simulation based on the Finite Element Analysis to study the properties of this noise. Moreover, we propose filtering process based on a modified version of graph-cuts segmentation method to remove the influence of this noise. Simulated data together with an experimental data-set obtained from a real RFEC inspection show the validity of the proposed approach.
Gerardo-Castro, M.P., Peynot, T., Ramos, F., Fitch, R. & IEEE 2014, 'Robust Multiple-Sensing-Modality Data Fusion using Gaussian Process Implicit Surfaces', 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION).
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R. & Andrade-Cetto, J. 2014, 'Exploration on Continuous Gaussian Process Frontier Maps', ICRA 2014 Proceeding, The 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, pp. 6077-6082.
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An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in this paper. The approach conveniently handles sparse sensor measurements to build a continuous model of the environment that exploits structural dependencies without the need to resort to a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps.
Hassan, M., Liu, Huang & Dissanayake 2014, 'Task Oriented Area Partitioning and Allocation for Optimal Operation of Multiple Industrial Robots in Unstructured Environments', International Conference on Control, Automation, Robotics and Vision, ICARCV, IEEE Xplore, Marina Bay Sands, Singapore, pp. 1184-1189.
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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.
Kanzhi, W.U., RANASINGHE, R. & DISSANAYAKE, G. 2014, 'A Fast Pipeline for Textured Object Recognition in Clutter using an RGB-D Sensor', Control Automation Robotics Vision (ICARCV), 2014 13th International Conference on, International Conference on Control, Automation, Robotics and Vision, IEEE, Marina Bay Sands, Singapore, pp. 1650-1655.
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This paper presents a modular algorithm pipeline for recognizing textured household objects in cluttered environment and estimating 6 DOF poses using an RGB-D sensor. The method draws from recent advances in this area and introduces a number of innovations that enable improved performances and faster operational speed in comparison with the state-of-the-art. The pipeline consists of (i) support plane subtraction (ii) SIFT feature extraction and approximate nearest neighbour based
matching (iii) feature clustering using 3D Eculidean distances (iv) SVD based pose estimation in combination with a outlier rejection strategy named SORSAC ( Spatially ORdered RAndom Consensus ) and (v) a pose combination and refinement step to
combine overlapping identical instances and to refine the pose estimation result by removing incorrect hypothesis. Quantitative comparisons with the MOPED [1] system on self-constructed dataset are presented to demonstrate the effectiveness of the pipeline.
Khosoussi, K., Huang, S. & Dissanayake, G. 2014, 'Novel insights into the impact of graph structure on SLAM', IEEE International Conference on Intelligent Robots and Systems, 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.
Kirchner, N., Alempijevic, A., Virgona, A., Dai, X., Ploger, P.G. & Venkat, R.K. 2014, 'A robust people detection, tracking, and counting system', Australian Conference on Robotics and Automation 2014, 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 system 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 the people in real world. To accomplish the people counting task, a robust system in 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 sensors self localization, 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, J.J.H., Frey, K., Fitch, R. & Sukkarieh, S. 2014, 'Fast path planning for precision weeding', Australasian Conference on Robotics and Automation, ACRA.
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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, D.K., Dissanayake, G., Valls Miro, J. & Waldron, K.J. 2014, 'Infrastructure robotics: Research challenges and opportunities', 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings, pp. 43-49.
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Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney.
Martin, F., Valls Miro, J. & Moreno, L. 2014, 'Towards Exploiting the Advantages of Colour in Scan Matching', Springer International Publishing, pp. 217-231.
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Colour plays an important role in the perception systems of
the human beings. In robotics, the development of new sensors has made
it possible to obtain colour information together with depth information
about the environment. The exploitation of this type of information has
become more and more important in numerous tasks. In our recent work,
we have developed an evolutionary-based scan matching method. The
aim of this work is to modify this method by the introduction of colour
properties, taking the first steps in studying how to use colour to improve
the scan matching. In particular, we have applied a colour transition
detection method based on the delta E divergence between neighbours
in a scan. Our algorithm has been tested in a real environment and
significant conclusions have been reached.
Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2014, 'Mobile Robotic Wireless Sensor Networks for Efficient Spatial Prediction', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Chicago, IL, USA, pp. 1176-1181.
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This paper addresses the issue of monitoring physical spatial phenomena of interest utilizing the information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. The spatial phenomenon is statistically modelled by a Gaussian Markov Random Field (GMRF) with hyperparameters that are learnt as the measurements accumulate over
time. In this context, the GMRF approximately represents the spatial field on an irregular lattice of triangulation by exploiting a stochastic partial differential equation (SPDE) approach, which benefits remarkably in computation due to the sparsity
of the precision matrix. A technique of the one-step-ahead forecast is employed to predict the future measurements that are required to find the optimal sampling locations. It is shown that optimizing the sampling path problem with the logarithm
of the determinant either of a covariance matrix using a GP model or of a precision matrix using a GMRF model for mobile robotic wireless sensor networks (MRWSNs) even by a greedy algorithm is impractical. This paper proposes an efficient novel
optimality criterion for the adaptive sampling strategy to find the most informative locations in taking future observations that minimize the uncertainty at unobserved locations. The computational complexity of our proposed method is linear, which makes the MRWSN scalable and practically feasible. The effectiveness of the proposed approach is compared and demonstrated using a pre-published data set with appealing results.
Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2014, 'Spatially-Distributed Prediction with Mobile Robotic Wireless Sensor Networks', 2014 13th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics & Vision, Institute of Electrical and Electronics Engineers Inc., Marina Bay Sands, Singapore, pp. 1153-1158.
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This paper presents a distributed spatial estimation and prediction approach to address the centrally-computed scheme of Gaussian Process regression at each robotic sensor in resource-constrained networks of mobile, wireless and noisy agents monitoring physical phenomena of interest. A mobile sensor independently estimate its own parameters using collective measurements from itself and local neighboring agents as they navigate through the environment. A spatially-distributed prediction algorithm is designed utilizing methods of Jacobi overrelaxation and discrete-time average consensus to enable a robotic sensor to update its estimation of obtaining the global model parameters and recursively compute the global goal of inference. A distributed navigation strategy is also considered to drive sensors to the most uncertain locations enhancing the quality of prediction and learning parameters. Experimental results in a real-world data set illustrate the effectiveness of the proposed approach and is highly comparable to those of the centralized scheme.
Nguyen, V., Kodagoda, S., Ranasinghe, R., Dissanayake, G., Bustamante, H., Vitanage, D. & Nguyen, T. 2014, 'Spatial Prediction of Hydrogen Sulfide in Sewers with a Modified Gaussian Process Combined Mutual Information', 2014 13th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics & Vision, Institute of Electrical and Electronics Engineers Inc., Marina Bay Sands, Singapore, pp. 1130-1135.
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This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H2S) in a gravity sewer system. The gaseous H2S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths of the sewer network. A mutual information based strategy is used to choose the best k sensor measurements and their locations from among n potential sensor observations and their locations. This provably NP-hard combinatorial sensor selection problem is addressed by maximizing the mutual information between the selected locations and the locations that are not selected or do not have any sensor deployments. A proof-of-concept study was carried out comparing the spatial prediction of H2S with a complex model currently used by Sydney Water. The proposed approach is shown to be effective in both modelling and predicting the H2S spatial concentrations in sewers as well as identifying optimal number of H2S sensors and their locations for a required level of prediction accuracy.
Norouzi, M., Valls Miro, J., Dissanayake, G. & Vidal-Calleja, T. 2014, 'Path planning with stability uncertainty for articulated mobile vehicles in challenging environments', IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 1748-1753.
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This article proposes a probabilistic approach to account for robot stability uncertainty when planing motions over uneven terrains. A novel probabilistic stability criterion derived from the cumulative distribution of a tip-over metric is introduced that allows a safety constraint to be dynamically updated by available sensor data as it becomes available. The proposed safety constraint authorizes the planner to generates more conservative motion plans for areas with higher levels of uncertainty, while avoids unnecessary caution in well-known areas. The proposed systematic approach is particularly applicable to reconfigurable robots that can assume safer postures when required, although is equally valid for fixed-configuration platforms to choose safer paths to follow. The advantages of planning with the proposed probabilistic stability metric are demonstrated with data collected from an indoor rescue arena, as well as an outdoor rover testing facility.
Patel, M., Valls Miro, J. & Dissanayake, D. 2015, 'A Probabilistic Approach to Learn Activities of Daily Living of a Mobility Aid Device User', Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation, IEEE, Hong Kong, pp. 969-974.
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The problem of inferring human behaviour is naturally complex: people interact with the environment and each other in many different ways, and dealing with the often incomplete and uncertain sensed data by which the actions are perceived only compounds the difficulty of the problem. In this paper, we propose a framework whereby these elaborate behaviours can be naturally simplified by decomposing them into smaller activities, whose temporal dependencies can be more efficiently represented via probabilistic hierarchical learning models. In this regard, patterns of a number of activities typically carried out by users of an ambulatory aid device have been identified with the aid of a Hierarchical Hidden Markov Model (HHMM) framework. By decomposing the complex behaviours into multiple layers of abstraction the approach is shown capable of modelling and learning these tightly coupled human-machine interactions. The inference accuracy of the proposed model is proven to compare favourably against more traditional discriminative models, as well as other compatible generative strategies to provide a complete picture that highlights the benefits of the proposed approach, and opens the door to more intelligent assistance with a robotic mobility aid.
Piyathilaka, J.M. & Kodagoda 2014, 'Affordance-Map : A Map for Context-Aware Path Planning.', Proceedings of the Australasian Conference on Robotics and Automation 2014, Australasian Conference on Robotics and Automation 2014, 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 context-
awareness 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 them-
selves are a non-trivial task to detect. Once learned, affordance-map allows us to assign anaffordance 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.
Piyathilaka, J.M. & Kodagoda, S. 2014, 'Active Visual Object Search Using Affordance-Map in Real World : A Human-Centric Approach', Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), The 13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, 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
Poon, J.T. & Valls Miro, J. 2014, 'A Multi-Modal Utility to Assist Powered Mobility Device Navigation Tasks', Social Robotics: 6th International Conference, Icsr 2014, Sydney, Nsw, Australia, October 27-29, 2014. Proceedings, 6th International Conference on Social Robotics, Springer International Publishing, Sydney, Australia, pp. 300-309.
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This paper presents the development of a shared control system for power mobility device users of varying capability in order to reduce carer oversight in navigation. Weighting of a user’s joystick input against a short-tem trajectory prediction and obstacle avoidance algorithm is conducted by taking into consideration proximity to obstacles and smoothness of user driving, resulting in capable users rewarded greater levels of manual control for undertaking maneuvres that can be considered more challenging. An additional optional comparison with a Vector Field Histogram applied to leader-tracking provides further activities, such as completely autonomous following and a task for the user to follow a leading entity. Indoor tests carried out on university campus demonstrate the viability of this work, with future trials at a care home for the disabled intended to show the system functioning in one of its intended settings.
Quin, P.D., Alempijevic, A., Paul, G. & Liu, D. 2014, 'Expanding Wavefront Frontier Detection: An Approach for Efficiently Detecting Frontier Cells', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/by_date.html, 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.
Sinha, A. & Wang, J.J. 2014, 'An implementation of the path integrator mechanism of head direction cells for bio-mimetic navigation', 2014 International Joint Conference on Neural Networks (IJCNN), 2014 International Joint Conference on Neural Networks, IEEE, Beijing, pp. 1984-1991.
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Head direction cells are thought to be an integral part of the neural navigation system. These cells track the agent's current head direction irrespective of the host's location. In doing so, they process a combination of inputs: angular velocity and visual inputs are major effectors; to correctly encode the agent's current heading. There are close to fifteen models of head direction cell systems found in literature today. Very few of these models have been implemented for bio-mimetic navigation in robots. In this paper, we describe an implementation of the head direction cell system on the robot operating system (ROS) robotic platform as a first step towards a bio-mimetic navigation system for Willow Garage's personal robot 2 (PR2) robot.
Sinha, A. & Wang, J.J. 2014, 'Bio-mimetic Path Integration Using a Self Organizing Population of Grid Cells', Artificial Neural Networks and Machine Learning – ICANN 2014, International Conference on Artificial Neural Networks, Springer International Publishing, Hamburg, Germany, pp. 675-682.
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Grid cells in the dorsocaudal medial entorhinal cortex (dMEC) of the rat provide a metric representation of the animal’s local environment. The collective firing patterns in a network of grid cells forms a triangular mesh that accurately tracks the location of the animal. The activity of a grid cell network, similar to head direction cells, displays path integration characteristics. Classical robotics use path integrators in the form of inertial navigation systems to track spatial information of an agent as well. In this paper, we describe an implementation of a network of grid cells as a dead reckoning system for the PR2 robot.
Sinha, A. & Wang, J.J. 2014, 'Bio-mimetic path integration using a self organizing population of grid cells', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 675-682.
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Grid cells in the dorsocaudal medial entorhinal cortex (dMEC) of the rat provide a metric representation of the animal's local environment. The collective firing patterns in a network of grid cells forms a triangular mesh that accurately tracks the location of the animal. The activity of a grid cell network, similar to head direction cells, displays path integration characteristics. Classical robotics use path integrators in the form of inertial navigation systems to track spatial information of an agent as well. In this paper, we describe an implementation of a network of grid cells as a dead reckoning system for the PR2 robot. © 2014 Springer International Publishing Switzerland.
Skinner, B., Vidal Calleja, T., Valls Miro, J., de Bruijn, F. & Falque, R. 2014, '3D Point Cloud Upsampling for Accurate Reconstruction of Dense 2.5D Thickness Maps.', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/at_a_glance.html, Australasian Conference on Robotics and Automation, Melbourne University.
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This paper presents a novel robust processing methodology for computing 2.5D thickness maps from dense 3D collocated surfaces. The proposed pipeline is suitable to faithfully adjust data representation detailing as required, from preserving fine surface features to coarse interpretations. The foundations of the proposed technique exploit spatial point-based filtering, ray tracing techniques and the Robust Implicit Moving Least Squares (RIMLS) algorithm applied to dense 3D datasets, such as those acquired from laser scanners. The effectiveness of the proposed technique in overcoming traditional angular aliasing and corruption artifacts is validated with 3D ranging data acquired from internal and external surfaces of exhumed water pipes. It is shown that the resulting 2.5D maps can be more accurately and completely computed to higher resolutions, while significantly reducing the number of raytracing errors when compared with 2.5D thickness maps derived from our current approach.
Su, D. & Valls Miro, J. 2014, 'An ultrasonic/RF GP-based sensor model robotic solution for indoors/outdoors person tracking', Proceedings of the 2014 13th International Conference on Control, Automation, Robotics & Vision, 2014 13th International Conference on Control, Automation, Robotics & Vision (ICARCV 2014), IEEE, Singapore, Singapore, pp. 1662-1667.
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An non-linear Bayesian regression engine for robotic tracking based on an ultrasonic/RF sensor unit is presented in this paper. The proposed system is able to maintain systematic tracking of a leading human in indoor/outdoor settings with minimalistic instrumentation. Compared to popular camera based localization system the sonar array/RF based system has the advantage of being insensitive to background light intensity changes, a primary concern in outdoor environments. In contrast to single-plane laser range finder based tracking the proposed scheme is able to better adapt to small terrain variations, while at the same time being a significantly more affordable proposition for tracking with a robotic unit. A key novelty in this work is the utilisation of Gaussian Process Regression (GPR) to build a model for the sensor unit, which is shown to compare favourably against traditional linear triangulation approaches. The covariance function yield by the GPR sensor model also provides the additional benefit of outlier rejection. We present experimental results of indoors and outdoors tracking by mounting the sensor unit on a Garden Utility Transportation System (GUTS) robot and compare the proposed approach with linear triangulation which clearly show the inference engine capability to generalise relative localisation of human and a marked improvement in tracking accuracy and robustness.
To, A.W., Paul, G., Rushton-Smith, D., Liu, D. & Dissanayake, G. 2012, 'Automated and Frequent Calibration of a Robot Manipulator-mounted IR Range camera for Steel Bridge Maintenance', Field and Service Robotics Vol 92 - Results of the 8th International Conference on Field and Service Robotics, International Conference on Field and Service Robotics, Springer-Verlag, Matsushima, Miyagi, Japan, pp. 205-218.
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This paper presents an automated and cost-effective approach to frequent hand-eye calibration of an IR range camera mounted to the end-effector of a robot manipulator for use in a field environment. A set of three reflector discs arranged in a structured pattern attached to the robot platform is used to provide high contrast image features with corresponding range readings for accurate calculation of the camera-to-robot base transform. Using this approach the hand-eye transform between the IR range camera and robot end-effector can be determined by considering the robot manipulator model. Experimental results show that a structured lightingbased IR range camera can be reliably hand-eye calibrated to a 6DOF robot manipulator using the presented automated approach. Once calibrated, the IR range camera can be positioned with the manipulator so as to generate a high resolution geometric map of the surrounding environment suitable for performing the grit-blasting task.
Ulapane, N., Alempijevic, A., Vidal-Calleja, T., Valls Miro, J., Rudd, J. & Roubal, M. 2014, 'Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling', Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on, pp. 1762-1767.
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Vidal Calleja, T.A., Su, D., De Bruijn, F. & Valls Miro, J. 2014, 'Learning Spatial Correlations for Bayesian fusion in Pipe Thickness Mapping', 2014 IEEE International Conference on Robotics and Automation, 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), IEEE, Hong Kong, pp. 1-8.
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Vidal Calleja, T.A., Valls Miro, J., Martin, F., Lingnau, D. & Russell, D. 2014, 'Automatic Detection and Verification of Pipeline Construction Featureswith Multi-modal data', 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, Chicago, IL, USA, pp. 3116-3122.
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Assessment of the condition of underground pipelines is crucial to avoid breakages. Autonomous in-line inspection tools provided with Non-destructive Technology (NDT) sensors to assess large sections of the pipeline are commonly used for these purposes. An example of such sensors based on Eddy currents is the Remote Field Technology (RFT). A crucial step during in-line inspections is the detection of construction features, such as joints and elbows, to accurately locate and size specific defects within pipe sections. This step is often performed manually with the aid of visual data, which results in slow data processing. In this paper, we propose a generic framework to automate the detection and verification of these construction
features using both NDT sensor data and visual images. Firstly, supervised learning is used to identify the construction features in the NDT sensor signals. Then, image processing is employed to verify the selection. Results are presented with data from a
RFT tool, for which a specialised descriptor has been designed to characterise and classify its signal features. Furthermore, the construction feature is displayed in the image, once it is identified in the RFT data and detected in the visual data. A visual odometry algorithm has been implemented to locate the visual data with respect to the RFT data. About 800 meters of these multi-modal data are evaluated to test the validity of the proposed approach.
Vidal-Calleja, T., Su, D., De Bruijn, F. & Miro, J.V. 2014, 'Learning Spatial Correlations for Bayesian Fusion in Pipe Thickness Mapping', 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE International Conference on Robotics and Automation (ICRA), IEEE, Hong Kong, PEOPLES R CHINA, pp. 683-690.
Waldron, K. 2014, 'A Study of the Complete Stride Cycle in dynamically Stable Quadrupedal Locomotion', Mobile Service Robotics, 17th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, World Scientific, Poznan, Poland, pp. 223-230.
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An understanding of the gross mechanics of running is essential for the design of running robots that use dynamically stable gaits. In earlier papers [1, 2] the author and his colleagues analyzed the complete stride cycle for both transverse and rotary gallops. This resulted in a solution that required that the durations of the two flight phases should be equal, in both cases. Examination of experimental results indicates that this conclusion is quite wrong. Review of the analysis indicates that this result was driven by an assumption that the system behaves as a rigid body for motion about the roll axis. Abandoning that assumption produces a simpler analysis which produces results that are broadly consistent with available experimental data.
Wang, Y. & Huang, S. 2014, 'Motion Segmentation based Robust RGB-D SLAM', World Congress on Intelligent Control and Automation (WCICA), Shenyang, China.
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Wang, Y. & Huang, S. 2014, 'Towards Dense Moving Object Segmentation based Robust Dense RGB-D SLAM in Dynamic Scenarios', Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on, The 13th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, Singapore.
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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 get the camera poses. Quantitative results from the available challenging benchmark dataset have proved the effectiveness of our method.
Ward, P.K., Quin, P., Pagano, D., Yang, C., Liu, D., Waldron, K., Dissanayake, D., Paul, G., Brooks, P., Mann, P., Kaluarachchi, W., Manamperi, P. & Matkovic, L. 2014, 'Climbing Robot for Steel Bridge Inspection: Design Challenges', Austroads Publications Online, 9th Austroads Bridge Conference, ARRB Group, New South Wales.
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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.
Zhang, Huang & Liu 2014, 'Comparison of Two Strategies of Path Planning for Underwater Robot Navigation Under Uncertainty', the 13th International Conference on Control, Automation, Robotics and Vision (ICARCV 2014), IEEE, Singapore.
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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, Y., Zhao, Y., Xiong, R., Wang, Y., Wang, J.J. & Chu, J. 2014, 'Spin Observation and Trajectory Prediction of a Ping-Pong Ball', 2014 IEEE International Conference on Robotics & Automation (ICRA), Hong Kong, China, pp. 4108-4114.
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For ping-pong playing robots, observing a ball and
predicting a ball’s trajectory accurately in real-time is essential.
However, most existing vision systems can only provide ball’s
position observation, and do not take into consideration the
spin of the ball, which is very important in competitions. This
paper proposes a way to observe and estimate ball’s spin in
real-time, and achieve an accurate prediction. Based on the
fact that a spinning ball’s motion can be separated into global
movement and spinning respect to its center, we construct an
integrated vision system to observe the two motions separately.
With a pan-tilt vision system, the spinning motion is observed
through recognizing the position of the brand on the ball and
restoring the 3D pose of the ball. Then the spin state is estimated
with the method of plane fitting on current and historical
observations. With both position and spin information, accurate
state estimation and trajectory prediction are realized via
Extended Kalman Filter(EKF). Experimental results show the
effectiveness and accuracy of the proposed method.
Zhao, L., Huang, S. & Dissanayake, G. 2014, 'Linear MonoSLAM: A Linear Approach to Large-Scale Monocular SLAM Problems', 2014 IEEE International Conference on Robotics & Automation (ICRA), IEEE, Hong Kong, China, pp. 1517-1523.
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This paper presents a linear approach for solving monocular simultaneous localization and mapping (SLAM) problems. The algorithm rst builds a sequence of small initial submaps and then joins these submaps together in a divideand-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.