Abeywardena, D.M., Kodagoda, S., Dissanayake, G. & Munasinghe, R. 2013, 'Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator', IEEE Robotics and Automation Magazine, vol. 20, no. 4, pp. 32-39.
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In this article, we presented a novel state estimator for quadrotor MAVs, where clear improvements in estimates stemming from the incorporation of quadrotor-specific dynamical constraints were demonstrated. Our design is based on an EKF and is capable of estimating both roll and pitch angles of the attitude, in addition to X and Y components of the body frame translational velocities within a bounded error. This estimator is applied to inertial data gathered from real-world flight experiments. The resulting attitude and velocity estimates obtained match closely with the ground truth and are drift free.
Brunner, C., Peynot, T., Vidal Calleja, T.A. & Underwood, J. 2013, 'Selective Combination of Visual and Thermal Imaging for Resilient Localization in Adverse Conditions: Day and Night, Smoke and Fire', Journal of Field Robotics, vol. 30, no. 4, pp. 641-666.
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Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.
Cai, B., Huang, S., Liu, D., Yuan, S., Dissanayake, G., Lau, H. & Pagac, D. 2013, 'Multi-objective optimization for autonomous straddle carrier scheduling at automated container terminals', IEEE Transactions on Automation Science and Engineering, vol. 10, no. 3, pp. 711-725.
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A multiobjective optimization model is presented in this paper for the Autonomous Straddle Carriers Scheduling (ASCS) problem in automated container terminals, which is more practical than the single objective model. The model considers three objectives [i.e., Straddle Carriers (SCs) traveling time, SC waiting time and finishing time of high-priority container-transferring jobs], and their weighted sum is investigated as the representative example. The presented model is formulated as a pickup and delivery problem with time windows in the form of binary integer programming. An exact algorithm based on Branch-and-Bound with Column Generation (BBCG) is employed for solving the multiobjective ASCS problem. Based on the map of an actual fully automated container terminal, simulation results are compared with the single-objective scheduling to demonstrate the effectiveness and flexibility of the presented multiobjective model, as well as the efficacy of the BBCG algorithm for autonomous SC scheduling.
Carmichael, M.G. & Liu, D. 2013, 'Estimating Physical Assistance Need Using a Musculoskeletal Model', IEEE Transactions On Biomedical Engineering, vol. 60, no. 7, pp. 1912-1919.
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Technologies that provide physical assistance during tasks are often required to provide assistance specific to the task and person performing it. An example is robotic rehabilitation in which the assistance-as-needed (AAN) paradigm aims to provide operators with the minimum assistance required to perform the task. Current approaches use empirical performance-based methods which require repeated observation of the specific task before an estimate of the needed assistance can be determined. In this paper, we present a new approach utilizing a musculoskeletal model (MM) of the upper limb to estimate the operator's assistance needs with respect to physical tasks. With capabilities of the operator defined at the muscular level of the MM, an optimization model is used to estimate the operator's strength capability. Strength required to perform a task is calculated using a task model. The difference or gap between the operator's strength capability and the strength required to execute a task forms the basis for the new AAN paradigm. We show how this approach estimates the effects of limb pose, load direction, and muscle impairments on a person's ability to perform tasks.
Khushaba, R.N., Kodagoda, S., Lal, S. & Dissanayake, G. 2013, 'Uncorrelated fuzzy neighborhood preserving analysis based feature projection for driver drowsiness recognition', Fuzzy Sets and Systems, vol. 221, no. 1, pp. 90-111.
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Driver drowsiness is reported as one of the main causal factors in many traffic accidents as it progressively impairs the driver's awareness about external events. Drowsiness detection can be approached through monitoring physiological signals while driving to correlate drowsiness with the change in the corresponding patterns of the Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals. The main challenge in such an approach is to extract a set of features that can highly discriminate between the different drowsiness levels. This paper proposes a new Fuzzy Neighborhood Preserving Analysis (FNPA) feature projection method that is used to extract the discriminant information relevant to the loss of attention caused by drowsiness. Unlike existing methods, FNPA considers the fuzzy memberships of the input measurements into the different classes while constructing the graph Laplacian. Thus, it is able to identify both the discriminant and the geometrical structure of the input data while accounting for the overlapping nature of the drowsiness patterns. Furthermore, in order to address the singularity problem that occurs in many real world problems, the singular value decomposition (SVD), and later the QR-Decomposition, are utilized to extract a set of statistically uncorrelated features presenting the Uncorrelated FNPA (UFNPA). In the current preliminary study with datasets collected from 31 subjects only, while performing a driving simulation task, the proposed method is capable of accurately classifying the drowsiness levels using a small number of features with an average accuracy of 93%93%. On the other hand, the possibility of developing a subject-independent drowsiness recognition system is also investigated when the problem is converted into a binary classification task, as imposed by the number of drowsiness levels exhibited by the drivers, with accuracies ranging from 82%-to-84%.
Khushaba, R.N., Kodagoda, S., Liu, D. & Dissanayake, G. 2013, 'Muscle Computer Interfaces for Driver Distraction Reduction', Computer Methods and Programs in Biomedicine, vol. 110, no. 2, pp. 137-149.
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Driver distraction is regarded as a significant contributor to motor-vehicle crashes. One of the important factors contributing to driver distraction was reported to be the handling and reaching of in-car electronic equipment and controls that usually requires taking the drivers' hands off the wheel and eyes off the road. To minimize the amount of such distraction, we present a new control scheme that senses and decodes the human muscles signals, denoted as Electromyogram (EMG), associated with different fingers postures/pressures, and map that to different commands to control external equipment, without taking hands off the wheel. To facilitate such a scheme, the most significant step is the extraction of a set of highly discriminative feature set that can well separate between the different EMG-based actions and to do so in a computationally efficient manner. In this paper, an accurate and efficient method based on Fuzzy Neighborhood Discriminant Analysis (FNDA), is proposed for discriminant feature extraction and then extended to the channel selection problem. Unlike existing methods, the objective of the proposed FNDA is to preserve the local geometrical and discriminant structures, while taking into account the contribution of the samples to the different classes. The method also aims to efficiently overcome the singularity problems of classical LDA by employing the QR-decomposition. Practical real-time experiments with eight EMG sensors attached on the human forearm of eight subjects indicated that up to fourteen classes of fingers postures/pressures can be classified with <7% error on average, proving the significance of the proposed method.
Khushaba, R.N., Wise, C., Kodagoda, S., Louviere, J.J., Kahn, B.E. & Townsend, C. 2013, 'Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking', Expert Systems with Applications, vol. 40, no. 9, pp. 3803-3812.
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Paul, G., Kwok, N.M. & Liu, D. 2013, 'A novel surface segmentation approach for robotic manipulator-based maintenance operation planning', Automation In Construction, vol. 29, pp. 136-147.
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This paper presents a novel approach to segmenting a three-dimensional surface map by considering the task requirements and the movements of an industrial robot manipulator. Maintenance operations, such as abrasive blasting, that are performed by a field robot manipulator can be made more efficient by exploiting surface segmentation. The approach in this paper utilises an aggregate of multiple connectivity graphs, with graph edges defined by task constraints, and graph vertices that correspond to small, maintenance-specific target surfaces, known as Scale-Like Discs (SLDs). The task constraints for maintenance operations are based on the characteristics of neighbouring SLDs. The combined connectivity graphs are analysed to find clusters of vertices, thus segmenting the surface map into groups of related SLDs. Experiments conducted in three typical bridge maintenance environments have shown that the approach can reduce garnet usage by 10%â40% and reduce the manipulator joint movements by up to 35%.
Shi, L. & Kodagoda, S. 2013, 'Towards generalization of semi-supervised place classification over generalized Voronoi graph', Robotics And Autonomous Systems, vol. 61, no. 8, pp. 785-796.
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With the progress of humanrobot interaction (HRI), the ability of a robot to perform high-level tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels. Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rulebased approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semisupervised place classification over a generalized Voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized Voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected in international university environments.
Skinner, B., Yuan, S., Huang, S. & Liu, D. 2013, 'A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms', European Journal Of Operational Research, vol. 228, no. 1, pp. 72-82.
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This paper proposes a new crossover operator called two-part chromosome crossover (TCX) for solving the multiple travelling salesmen problem (MTSP) using a genetic algorithm (GA) for near-optimal solutions. We adopt the two-part chromosome representation technique which has been proven to minimise the size of the problem search space. Nevertheless, the existing crossover method for the two-part chromosome representation has two limitations. Firstly, it has extremely limited diversity in the second part of the chromosome, which greatly restricts the search ability of the GA. Secondly, the existing crossover approach tends to break useful building blocks in the first part of the chromosome, which reduces the GAâs effectiveness and solution quality. Therefore, in order to improve the GA search performance with the two-part chromosome representation, we propose TCX to overcome these two limitations and improve solution quality. Moreover, we evaluate and compare the proposed TCX with three different crossover methods for two MTSP objective functions, namely, minimising total travel distance and minimising longest tour. The experimental results show that TCX can improve the solution quality of the GA compared to three existing crossover approaches.
Skinner, B., Yuan, S., Huang, S., Liu, D., Cai, B., Dissanayake, G., Lau, H., Bott, A. & Pagac, D. 2013, 'Optimisation for job scheduling at automated container terminals using genetic algorithm', Computers and Industrial Engineering, vol. 64, no. 1, pp. 511-523.
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This paper presents a genetic algorithm (GA)-based optimisation approach to improve container handling operations at the Patrick AutoStrad container terminal located in Brisbane Australia. In this paper we focus on scheduling for container transfers and encode the problem using a two-part chromosome approach which is then solved using a modified genetic algorithm. In simulation experiments, the performance of the GA-based approach and a sequential job scheduling method are evaluated and compared with different scheduling scenarios. The experimental results show that the GA-based approach can find better solutions which improve the overall performance. The GA-based approach has been implemented in the terminal scheduling system and the live testing results show that the GA-based approach can reduce the overall time-related cost of container transfers at the automated container terminal
Wang, H., Huang, S., Frese, U. & Dissanayake, G. 2013, 'The nonlinearity structure of point feature SLAM problems with spherical covariance matrices', Automatica, vol. 49, pp. 3112-3119.
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This paper proves that the optimization problem of one-step point feature Simultaneous Localization and Mapping (SLAM) is equivalent to a nonlinear optimization problem of a single variable when the associated uncertainties can be described using spherical covariance matrices. Furthermore, it is proven that this optimization problem has at most two minima. The necessary and sufficient conditions for the existence of one or two minima are derived in a form that can be easily evaluated using observation and odometry data. It is demonstrated that more than one minimum exists only when the observation and odometry data are extremely inconsistent with each other. A numerical algorithm based on bisection is proposed for solving the one-dimensional nonlinear optimization problem. It is shown that the approach extends to joining of two maps, thus can be used to obtain an approximate solution to the complete SLAM problem through map joining.
Wu, H., Liu, H. & Liu, D. 2013, 'Two Dimensional Direction Recognition Using Uniaxial Tactile Arrays', IEEE Sensors Journal, vol. 13, no. 2, pp. 4897-4903.
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To allow intuitive communication in human-robot cooperation through tactile information, this paper presents a method to recognize human intended direction in 2-D using a handlebar equipped with uniaxial tactile arrays. The method first extracts various features from the tactile images aiming to reduce computation complexity and increase recognition robustness. A support vector machines classifier was implemented for classifying the intended direction of humans using the extracted features. The algorithm efficiency of using different combinations of features has been investigated and compared through human user studies. In total, five human users in the project team were involved in this research. Experimental results show that the proposed method can achieve 91.7% recognition accuracy if both the training data and validation data contain tactile images from all the users. The method could still achieve 77.5% recognition accuracy when the training and validation data share no common user.
Xu, Z., Fitch, R., Underwood, J. & Sukkarieh, S. 2013, 'Decentralized Coordinated Tracking with Mixed Discrete-Continuous Decisions', JOURNAL OF FIELD ROBOTICS, vol. 30, no. 5, pp. 717-740.
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Yang, C., Zhang, Q. & Huang, S. 2013, 'Input-to-state stability of a class of Lur'e descriptor systems', International Journal Of Robust And Nonlinear Control, vol. 23, no. 12, pp. 1324-1337.
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This paper considers a class of Lur'e descriptor systems (LDS) subject to exogenous disturbances. The concept of input-to-state stability (ISS) is generalized to descriptor systems. Such a notion characterizes the robust stability of the full state of the systems. Based on the conventional ISS theory, a sufficient condition expressed by linear matrix inequalities (LMIs) for the LDS to be ISS is derived. It is further shown that this condition also guarantees a special class of LDS to be of index one. Then, a state feedback controller is designed to make the closed-loop system ISS. Finally, an example is given to illustrate the obtained results.
Abeywardena, D.M., Wang, Z., Kodagoda, S. & Dissanayake, G. 2013, 'Visual-Inertial Fusion for Quadrotor Micro Air Vehicles with Improved Scale Observability', IEEE International Conference on Robotics and Automation, International Conference on Robotics and Automation, IEEE, Karlsruhe-Germany, pp. 3148-3153.
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This paper presents a novel algorithm for fusing monocular vision and inertial information for quadrotor Micro Air Vehicles by incorporating the unique dynamic characteristics of that platform into the state estimation process. The dynamics of a quadrotor is unique in that a dual axis accelerometer mounted parallel to the propeller plane provides measurements that are directly proportional to vehicle velocities in that plane. By exploiting these dynamic characteristics, we show that all vehicle states, including the absolute scale, become observable in all motion patterns. This distinguishes our method with other visual-inertial fusion methods, which either assume zero accelerometer bias, or require sufficiently exciting motion, such as non-zero acceleration, to ensure observability of the scale. The advantages of our method over existing visual-inertial fusion algorithms are proved through a theoretical analysis using Lie Derivatives and verified by extensive simulations and experiments.
Alempijevic, A., Fitch, R. & Kirchner, N.G. 2013, 'Bootstrapping Navigation and Path Planning Using Human Positional Traces', IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 1234-1239.
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Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework.
Alempijevic, A., Fitch, R., Kirchner, N. & IEEE 2013, 'Bootstrapping Navigation and Path Planning Using Human Positional Traces', 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp. 1242-1247.
Carmichael, M.G. & Liu, D. 2013, 'Admittance Control Scheme for Implementing Model-based Assistance-As-Needed on a Robot', 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), EMBC 2013, IEEE, Osaka, Japan, pp. 870-873.
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A model-based assistance-as-needed paradigm has been developed to govern the assistance provided by an assistive robot to its operator. This paradigm has advantages over existing methods of providing assistance-as-needed for applications such as robotic rehabilitation. However, implementation of the model-based paradigm requires a control scheme to be developed which controls the robot so as to provide the assistance calculated by the model-based paradigm to its operator. In this paper an admittance control scheme for providing model-based assistance-as-needed is presented. It is developed considering its suitability for human-robot interaction, and its role within the model-based assistance-as-needed framework. Results from the control implemented on an example robot showed it is capable of providing the operator with the desired level of assistance as governed by the model-based paradigm. This is an essential requirement for the paradigm to be capable of providing efficacious assistance-as-needed in applications such as robotic rehabilitation.
Carmichael, M.G. & Liu, D. 2013, 'Experimental Evaluation of a Model-based Assistance-As-Needed Paradigm using an Assistive Robot', 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Osaka, Japan, pp. 866-869.
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In robotic rehabilitation a promising paradigm is assistance-as-needed. This is because it promotes patient active participation which is essential for neuro-rehabilitation. A model-based assistance-as-needed paradigm has been developed which utilizes a musculoskeletal model representing the subject to calculate their assistance needs. In this paper we experimentally evaluate this model-based paradigm to control an assistive robot and provide a subject with assistance-as-needed at the muscular level. A subject with impairments defined in specific muscle groups performs a number of upper limb tasks, whilst receiving assistance from a robotic exoskeleton. The paradigm is evaluated on its ability to provide assistance only as the subject needs, depending on the tasks being performed and the impairments defined. Results show that the model-based assistance-as-needed paradigm was relatively successful in providing assistance when it was needed.
Dantanarayana, L.I., Ranasinghe, R. & Dissanayake, G. 2013, 'C-LOG: A Chamfer Distance Based Method for Localisation in Occupancy Grid-maps', IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE, Tokyo, Japan, pp. 376-381.
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In this paper, the problem of localising a robot within a known two-dimensional environment is formulated as one of minimising the Chamfer Distance between the corresponding occupancy grid map and information gathered from a sensor such as a laser range finder. It is shown that this nonlinear optimisation problem can be solved efficiently and that the resulting localisation algorithm has a number of attractive characteristics when compared with the conventional particle filter based solution for robot localisation in occupancy grids. The proposed algorithm is able to perform well even when robot odometry is unavailable, insensitive to noise models and does not critically depend on any tuning parameters. Experimental results based on a number of public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm.
Fang, G., Kwok, N. & Dissanayake, G. 2013, 'Skin colour detection using the statistical decision theory', Advanced Materials Research - Proceedings of the 4th International Conference on Manufacturing Science and Engineering, International Conference on Manufacturing Science and Engineering, Scientific.net, Dalian, China, pp. 1891-1895.
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Skin colour is an important attribute that can be used to detect human presence in an image. In this paper, a new method is introduced to detect skin pixels in an image based on statistical decision theory. The proposed method uses a parametric model to
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R., Andrade-Cetto, J. & Dissanayake, G. 2013, 'Exploration in Information Distribution Maps', Robotics Science and Systems - Workshop on Robotic Exploration, Monitoring and Information Collection, Robotics Science and Systems, Technische Universitat Berlin, Berlin, Germany.
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In this paper, a novel solution for autonomous robotic exploration is proposed. The distribution of information in an unknown environment is modeled as an unsteady diffusion process, which can be an appropriate mathematical formulation and analogy for expanding, time-varying, and dynamic environments. This information distribution map is the solution of the diffusion process partial differential equation, and is regressed from sensor data as a Gaussian Process. Optimization of the process parameters leads to an optimal frontier map which describes regions of interest for further exploration. Since the presented approach considers a continuous model of the environment, it can be used to plan smooth exploration paths exploiting the structural dependencies of the environment whilst handling sparse sensors measurements. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps.
Ghaffari Jadidi, M., Valls Miro, J., Valencia, R., Andrade-Cetto, J. & Dissanayake, G. 2013, 'Exploration using an Information-Based Reaction-Diffusion Process', Proceedings of the 2013 Australasian Conference on Robotics & Automation, Australasian Conference on Robotics and Automation, Australian Robtocis and Automation Association, Sydney, Australia, pp. 1-10.
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Exploration using an Information-Based Reaction-Diffusion Process
Hu, G., Khosoussi, K. & Huang, S. 2013, 'Towards a Reliable SLAM Back-End', IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Tokyo, Japan, pp. 37-43.
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In the state-of-the-art approaches to SLAM, the problem is often formulated as a non-linear least squares. SLAM back-ends often employ iterative methods such as Gauss-Newton or Levenberg-Marquardt to solve that problem. In general, there is no guarantee on the global convergence of these methods. The back-end might get trapped into a local minimum or even diverge depending on how good the initial estimate is. Due to the large noise in odometry data, it is not wise to rely on dead reckoning for obtaining an initial guess, especially in long trajectories. In this paper we demonstrate how M-estimation can be used as a bootstrapping technique to obtain a reliable initial guess. We show that this initial guess is more likely to be in the basin of attraction of the global minimum than existing bootstrapping methods. As the main contribution of this paper, we present new insights about the similarities between robustness against outliers and robustness against a bad initial guess. Through simulations and experiments on real data, we substantiate the reliability of our proposed method.
Khushaba, R.N., Wise, C., Kodagoda, S. & Louviere, J.J. 2013, 'Integrating Eye-Tracking and Wireless Electroencephalogram (EEG) in Consumer Neuroscience', The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC13), The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC13), IEEE, Osaka, Japan, pp. 6925-6928.
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Consumer neuroscience addresses marketing relevant problems through the integration and application of neuroscientific theories, concepts, findings and methods to the research discipline of consumer behavior. The key contribution of this paper is to complement the advancement of traditional consumer research through the investigation of the patterns of interdependency between the Electroencephalogram (EEG) signals from the different brain regions while participants undertook a choice task designed to elicit preferences for a marketing product (crackers). Specifically, the task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping).We analyze the Electroencephalogram (EEG) signals collected from the different brain regions using the commercially available 14 channel Emotiv EPOC wireless EEG headset and relate the EEG data to the specific choice options with a Tobii X60 eye tracker. Fifteen participants were recruited for this experiment and were shown 57 choice sets; each choice set described three choice options. The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0 - 4 Hz), Theta (3 - 7 Hz), Alpha (8 - 12 Hz), Beta (13 - 30 Hz), and Gamma (30 - 40 Hz). Our results indicate significant phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the choice task. Our experimental results also show that participants spent more time looking at the non-preferred items in each choice set at the beginning of the experiment (exploration mode), while reducing that time progressively to indicate significant amount of cognitive processing assigned to preferred items (exploitation mode).
Kodagoda, S., Alempijevic, A., Huang, S., De La Villefromoy, M.J., Diponio, M. & Cogar, L.J. 2013, 'Innovative Assessment of Mechatronic Subjects Using Remote Laboratories', International Conference on Information Technology Based Higher Education and Training, IEEE, Antalya, Turkey, pp. 1-5.
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In response to the rapid growth of online teaching and learning, University of Technology, Sydney (UTS) has been developing a number of remotely accessible laboratories. In this paper, we present our newly developed remote lab robotic rig that uniquely addresses challenges in Mechatronic courses. The rig contains a mobile robotic platform equipped with various sensory modules placed in a maze with a pantograph power system enabling continuous use of the platform. The software architecture employed allows users to develop their simulations using the Player/Stage simulator and subsequently upload the code in the robotic rig for real-time testing. This paper presents the motivation, design concepts and analysis of students' feedback responses to their use of the remote lab robotics rig. Survey results of a pilot study shows the participants highly agreeing that the remote lab contributes to, deeper understanding of the subject matter, flexible learning process and inspire research in robotics.
Kodagoda, S., Alempijevic, A., Huang, S., De La Villefromoy, M.J., Diponio, M. & Cogar, L.J. 2013, 'Moving Away from Simulations: Innovative Assessment of Mechatronic Subjects Using Remote Laboratories', 2013 International Conference on Information Technology Based Higher Education and Training, International Conference on Information Technology Based Higher Education and Training, IEEE, Antalya, Turkey, pp. 1-5.
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In response to the rapid growth of online teaching and learning, University of Technology, Sydney (UTS) has been developing a number of remotely accessible laboratories. In this paper, we present our newly developed remote lab robotic rig that uniquely addresses challenges in Mechatronic courses. The rig contains a mobile robotic platform equipped with various sensory modules placed in a maze with a pantograph power system enabling continuous use of the platform. The software architecture employed allows users to develop their simulations using the Player/Stage simulator and subsequently upload the code in the robotic rig for real-time testing. This paper presents the motivation, design concepts and analysis of students' feedback responses to their use of the remote lab robotics rig. Survey results of a pilot study shows the participants highly agreeing that the remote lab contributes to, deeper understanding of the subject matter, flexible learning process and inspire research in robotics
Kuo, V. & Fitch, R. 2013, 'Zero mutual interference network for intelligent vehicle communication', IEEE Intelligent Vehicles Symposium, Proceedings, pp. 128-133.
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We propose to enable high-throughput, scalable communication for cooperative autonomous vehicles through zero mutual interference (ZMI) networks. Cooperation in complex environments relies on wireless communication, but conventional wireless networks are not designed for cooperative autonomous vehicles and are fundamentally limited by mutual interference. Our approach is to avoid mutual interference by design; ZMI networks provide wired network properties using wireless radio links. In this paper, we present the initial instantiation of a ZMI network based on a multi-radio, multichannel architecture. The network is constructed such that each vehicle communicates with topological neighbours using a dedicated radio and channel. We also present experimental results that compare the performance of a ZMI network to a conventional inter-vehicle communication network in a cooperative perception and control task. © 2013 IEEE.
Kuo, V., Fitch, R. & IEEE 2013, 'Zero Mutual Interference Network for Intelligent Vehicle Communication', 2013 IEEE INTELLIGENT VEHICLES SYMPOSIUM WORKSHOPS (IV WORKSHOPS), pp. 121-126.
Li, Y., Wang, J. & Kong, X. 2013, 'Zero velocity update with stepwise smoothing for inertial pedestrian navigation', International Global Navigation Satellite Systems Society 2013 Symposium Proceedings, International Global Navigation Satellite Systems Society, Menay Pty Ltd, Australia, Surfers Paradise, Australia, pp. 1-10.
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Zero velocity update (ZUPT) is an effective way to correct low cost inertial measurement unit (IMU) errors when it is foot-mounted for pedestrian navigation. The stance phase in steps provides zero velocity measurement for inertial sensor error correction. As the errors of IMU estimated position and velocity grow rapidly with time between each correction, ZUPT applied at each step leads to sharp corrections and discontinuities in the estimated trajectory. For motion analysis and visualization, these large corrections are undesirable. Consequently, the implementation of smoothing for ZUPT-aided INS is considered to eliminate the sharp corrections. In this paper, we propose a closed loop Rauch-Tung-Striebel (RTS) smoother using a 24 error states extended Kalman filter (EKF) implement on our previous pedestrian navigation systems. Unlike common RTS smoother which operates as off-line processing mode, a near-real-time stepwise smoother is implemented to eliminate the sharp corrections over the steps. The impact of the near real-time smoothing filter for different step manners (walk, run and climb stairs) combined with the Constant Velocity Update (CUPT) concept we proposed previously is illustrated and analysed. Experimental results show that the proposed method can dramatically improve pedestrian navigation smoothness.
Nguyen, J., Lawrance, N., Fitch, R., Sukkarieh, S. & IEEE 2013, 'Energy-Constrained Motion Planning for Information Gathering with Autonomous Aerial Soaring', 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp. 3825-3831.
Nguyen, V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2013, 'Locational Optimization based Sensor Placement for Monitoring Gaussian Processes Modeled Spatial Phenomena', Proc. 2013 IEEE 8th Conference on Industrial Electronics and Applications, 2013 IEEE 8th Conference on Industrial Electronics and Applications, IEEE, Melbourne, Australia, pp. 1-6.
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This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expectedvalue function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on realworld datasets have verified the superiority of the proposed approach.
Norouzi, M., Valls Miro, J. & Dissanayake, G. 2013, 'A Statistical Approach for Uncertain Stability Analysis of Mobile Robots', IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, IEEE, Germany, pp. 191-196.
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Stability prediction is an important concern for mobile robots operating in rough environments. Having the capacity to predict areas of instability means pro-actively being able to plan safer traversable paths. The most influential tip-over stability measures are based on two criteria, the robot's center of mass (CM) and the supporting polygon (SP) defined by the convex area spanned between the ground contact-points. However, there is significant uncertainty associated with many parameters in the planning pipe-line: the actual robot kino-dynamic model, its localisation in the ground, and the terrain models, particularly in uneven terrain. This article proposes a statistical analysis of stability prediction to account for some of the uncertainties. This is accomplished using the force angle (FA) stability measure for a reconfigurable multi-tracked vehicle fitted with flippers, a manipulator arm and a sensor head. Probability density function (PDF) of contact-points, CM and the FA stability measure are numerically estimated, with simulation results performed on the open dynamics engine (ODE) simulator based on uncertain parameters. Two techniques are presented: a conventional Monte Carlo scheme, and a structured unscented transform (UT) which results in significant improvement in computational efficiency. Experimental results on maps obtained from a range camera fitted on the sensor head while the robot traverses over a ramp and a series of steps are presented that confirms the validity of the proposed probabilistic stability prediction method.
Norouzi, M., Valls Miro, J. & Dissanayake, G. 2013, 'Planning Stable and Efficient Paths for Articulated Mobile Robots On Challenging Terrains', Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australasian Robotics and Automation Association, Sydney, Australia, pp. 1-10.
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An analytical strategy to generate stable paths for a reconfigurable vehicle while also meeting additional navigational objectives is herein proposed. The work is motivated by robots traversing over challenging terrains during search and rescue operations, such as those equipped with manipulator arms and/or flippers. The proposed solution looks at minimizing the length of the traversed path and the energy expenditure in changing postures, yet also accounts for additional constraints in terms of sensor visibility (i.e arm configurations close to those orthogonal to the horizontal global plane which can afford a wider sensor view) and traction (i.e. flipper angles that provide the largest trackterrain interaction area). The validity of the proposed planning approach is evaluated with a multitracked robot fitted with flippers and a range camera at the end of a manipulator arm while navigating over two challenging 3D terrain data sets: one in a mock-up urban search and rescue arena (USAR), and a second one from a publicly available quasi-outdoor rover testing facility (UTIAS).
Patel, M.N., Ek, C.H., Kyriazis, N., Argyros, A., Valls Miro, J. & Kragic, D. 2013, 'Language for Learning Complex Human-Object Interactions', 2013 IEEE International Conference on Robotics and Automation (ICRA), 2013 IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 4997-5002.
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In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user.
Patten, T., Fitch, R. & Sukkarieh, S. 2013, 'Large-scale near-optimal decentralised information gathering with multiple mobile robots', Australasian Conference on Robotics and Automation, ACRA.
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Information gathering at large spatial scales can be addressed with teams of decentralised robots. Many existing methods search over a limited time horizon and do not provide strong performance guarantees. Near-optimal methods that exploit submodular objective functions have been proposed, given a fixed time budget. We propose a revised problem formulation that seeks to near-optimally maximise information gain quickly. We present a novel, near-optimal polynomial-time decentralised algorithm for multiple robots and analyse the expected path length with respect to the number of robots, the size of the area, and the number of observations. Our approach is based on area partitioning and is practically beneficial in that it allows for superlinear speedup in the time required to maximise the submodular objective function, is decentralised, and is easy to implement. We show extensive simulation results that compare the performance of our algorithm to existing sequential allocation methods.
Peynot, T., Fitch, R., McAllister, R. & Alempijevic, A. 2012, 'Resilient Navigation through Probabilistic Modality Reconfiguration', Intelligent Autonomous Systems 12 - Volume 2 Proceedings of the 12th International Conference IAS-12 - Advances in Intelligent Systems and Computing, International Conference on Intelligent Autonomous Systems (IAS), Springer Verlag, Jeju Island, Korea, pp. 75-88.
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This paper proposes an approach to achieve resilient navigation for indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robots ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining the benefits of independent and complementary algorithmic approaches to navigation, or modalities, each tuned to a particular type of environment or situation. In this paper, the modalities comprise a path planning method and a reactive motion strategy. While the robot navigates, a Hidden Markov Model continually estimates the most appropriate modality based on two types of information: context (information known a priori) and monitoring (evaluating unpredictable aspects of the current situation). The robot then uses the recommended modality, switching between one and another dynamically. Experimental validation with a SegwayRMPbased platform in an office environment shows that our approach enables failure mitigation while maintaining the safety of the platform. The robot is shown to reach its goal in the presence of: 1) unpredicted control errors, 2) unexpected map errors and 3) a large injected localisation fault
Piyathilaka, J.M. & Kodagoda, S. 2013, 'Gaussian Mixture Based HMM for Human Daily Activity Recognition Using 3D Skeleton Features', Gaussian Mixture Based HMM for Human Daily Activity Recognition Using 3D Skeleton Features, 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA),, IEEE, Melbourne, pp. 567-572.
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Ability to recognize human activities will enhance the capabilities of a robot that interacts with humans. However automatic detection of human activities could be challenging due to the individual nature of the activities. In this paper, we present human activity detection model that uses only 3-D skeleton features generated from an RGB-D sensor (Microsoft Kinect). To infer the human activities, we implemented Gaussian Mixture Modal (GMM) based Hidden Markov model(HMM). GM outputs of the HMM were effectively able to capture multimodel nature of 3D positions of each skeleton joint. We tested our model in a publicly available data-set that consists of twelve different daily activities performed by four different people.The proposed model recorded recognition recall accuracy of 84% with previously seen people and 78% with previously unseen people.
Quin, P.D., Paul, G., Alempijevic, A., Liu, D. & Dissanayake, G. 2013, 'Efficient Neighbourhood-Based Information Gain Approach for Exploration of Complex 3D Environments', 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation (ICRA), IEEE, Karlsruhe, Germany, pp. 1343-1348.
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This paper presents an approach for exploring a complex 3D environment with a sensor mounted on the end effector of a robot manipulator. In contrast to many current approaches which plan as far ahead as possible using as much environment information as is available, our approach considers only a small set of poses (vector of joint angles) neighbouring the robot's current pose in configuration space. Our approach is compared to an existing exploration strategy for a similar robot. Our results demonstrate a significant decrease in the number of information gain estimation calculations that need to be performed, while still gathering an equivalent or increased amount of information about the environment.
Quin, P.D., Paul, G., Liu, D. & Alempijevic, A. 2013, 'Nearest Neighbour Exploration with Backtracking for Robotic Exploration of Complex 3D Environments', Proceedings of Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australian Robotics & Automation Association, Sydney, Australia, pp. 1-8.
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Australasian Conference on Robotics and Automation
Rushton-Smith, D., To, A.W., Paul, G. & Liu, D. 2013, 'An Accurate and Reliable Approach to Calibration of a Robot Manipulator-Mounted IR Range Camera for Field Applications', International Symposium on Robotics and Mechatronics, International Symposium on Robotics and Mechatronics, Research Publishing, Singapore, pp. 335-344.
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Ryu, K., Furukawa, T., Antol, S. & Dissanayake, G. 2013, 'Grid-based scan-to-map matching for accurate simultaneous localization and mapping: Theory and preliminary numerical study', Proceedings of the ASME Design Engineering Technical Conference.
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This paper presents a grid-based scan-to-map matching technique for accurate simultaneous localization and mapping (SLAM). At every acquisition of a new scan, the proposed technique estimates the relative position from which the previous scan was taken, and further corrects its estimation error by matching the new scan to the globally defined map. In order to achieve best scan-to-map matching at each acquisition, the map to match is represented as a grid map with multiple normal distributions (NDs) in each cell. Additionally, the new scan is also represented by NDs, developing a novel ND-to-ND matching technique. The ND-to-ND matching technique has significant potential in the enhancement of the global matching as well as the computational efficiency. Experimental results first show that the proposed technique successfully matches new scans to the map generating very small position and orientation errors, and then demonstrates the effectiveness of the multi-ND representation in comparison to the single-ND representation. Copyright © 2013 by ASME.
Sehestedt, S.A., Paul, G., Rushton-Smith, D. & Liu, D. 2013, 'Prior-knowledge Assisted Fast 3D Map Building of Structured Environments for Steel Bridge Maintenance', IEEE International Conference on Automation Science and Engineering, IEEE International Conference on Automation Science and Engineering, IEEE, Madison, WI, USA, pp. 1040-1046.
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Practical application of a robot in a structured, yet unknown environment, such as in bridge maintenance, requires the robot to quickly generate an accurate map of the surfaces in the environment. A consistent and complete map is fundamental to achieving reliable and robust operation. In a real-world and field application, sensor noise and insufficient exploration oftentimes result in an incomplete map. This paper presents a robust environment mapping approach using prior knowledge in combination with a single depth camera mounted on the end-effector of a robotic manipulator. The approach has been successfully implemented in an industrial setting for the purpose of steel bridge maintenance. A prototype robot, which includes the presented map building approach in its software package, has recently been delivered to industry.
Shi, L., Kodagoda, S. & Piccardi, M. 2013, 'Towards Simultaneous Place Classification and Object Detection based on Conditional Random Field with Multiple Cues', 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Tokyo, Japan, pp. 2806-2811.
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Valls Miro, J., Black, R., Andonovski, B. & Dissanayake, G. 2013, 'Development of a Novel Evidence-Based Automated Powered Mobility Device Competency Assessment', Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics, IEEE, Seattle, WA, USA, pp. 1-8.
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This paper describes the outcomes of a clinical study to assess the validity of a stand-alone sensor package and algorithms to aid the assessment by an occupational therapist (OT) whether a person has the capacity to safely and effectively operate a powered mobility device such as a wheelchair in their daily activities. The proposed solution consists of a suite of sensors capable of inferring navigational characteristics from the platform it is attached to (e.g. trajectories, map of surroundings, speeds, distance to doors, etc). Such information presents occupational therapists with the ability to augment their own observations and assessments with correlated, quantitative, evidence-based data acquired with the sensor array. Furthermore, OT reviews can take place at the therapist's discretion as the data from the trials is logged. Results from a clinical evaluation of the proposed approach, taking as reference the commonly-used Power-Mobility Indoor Driving Assessment (PIDA) assessment, were conducted at the premises of the Prince of Wales (PoW) Hospital in Sydney by four users, showing consistency with the OT scores, and setting the scene to a larger study with wider targeted participation.
Van Nguyen, L., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2013, 'Locational optimization based sensor placement for monitoring Gaussian processes modeled spatial phenomena', Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, pp. 1706-1711.
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This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expected-value function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on real-world datasets have verified the superiority of the proposed approach. © 2013 IEEE.
Waldron, K., Jacobs, D.A. & Park, L.J. 2013, 'An Actuated Continuous Spring Loaded Inverted Pendulum (SLIP) Model for the Analysis of Bouncing Gaits', Nature Inspired Mobile Robotics, CLAWAR 2013, World Scientific, Sydney, pp. 463-470.
Waldron, K., Ward, P., Liu, D.K. & Waldron, K.J. 2013, 'Optimal Design of a Magnetic Adhesion System for Climbing Robots', Nature Inspired Mobile Robotics, CLAWAR 2013, World Scientific, Sydney, pp. 375-382.
Wang, H., Hu, G., Huang, S. & Dissanayake, G. 2013, 'On the structure of nonlinearities in pose graph SLAM', Robotics: Science and Systems, pp. 425-432.
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© 2013 Massachusetts Institute of Technology. Pose graphs have become an attractive representation for solving Simultaneous Localization and Mapping (SLAM) problems. In this paper, we analyze the structure of the nonlinearities in the 2D SLAM problem formulated as the optimizing of a pose graph. First, we prove that finding the optimal configuration of a very basic pose graph with 3 nodes (poses) and 3 edges (relative pose constraints) with spherical covariance matrices, which can be formulated as a six dimensional least squares optimization problem, is equivalent to solving a one dimensional optimization problem. Then we show that the same result can be extended to the optimizing of a pose graph with "two anchor nodes" where every edge is connecting to one of the two anchor nodes. Furthermore, we prove that the global minimum of the resulting one dimensional optimization problem must belong to a certain interval and there are at most 3 minima in that interval. Thus the globally optimal pose configuration of the pose graph can be obtained very easily through the bisection method and closed-form formulas.
Wang, Y. & Huang, S. 2013, 'An Efficient Motion Segmentation Algorithm for Multibody RGB-D SLAM', Proceedings of Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australian Robotics and Automation Association, University of New South Wales, Sydney Australia, pp. 1-10.
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A simple motion segmentation algorithm using only two frames of RGB-D data is proposed, and both simulation and experimental segmentation results show its efficiency and reliability. To further verify its usability in multibody SLAM scenarios, we firstly apply it to a simulated typical multibody SLAM problem with only a RGB-D camera, and then utilize it to segment a real RGB-D dataset collected by ourselves. Based on the good results of our motion segmentation algorithm, we can get satisfactory SLAM results for the simulated problem and the segmentation results using real data also enable us to get visual odometry for each motion group thus facilitate the following steps to solve the practical multibody RGB-D SLAM problems.
Wang, Y., Xiong, R., Li, Q. & Huang, S. 2013, 'Kullback-Leibler Divergence based Graph Pruning in Robotic Feature Mapping', European Conference on Mobile Robots, European Conference on Mobile Robots, IEEE, Barcelona, Spain, pp. 32-37.
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In pose feature graph simultaneous localization and mapping, the robot poses and feature positions are treated as graph nodes and the odometry and observations are treated as edges. The size of the graph exerts an important influence on the efficiency of the graph optimization. Conventionally, the size of the graph is kept small by discarding the current frame if it is not spatially far enough from the previous one or not informative enough. However, these approaches cannot discard the already preserved frames when the robot re-visits the previously explored area. We propose a measure derived from Kullbach-Leibler divergence to decide whether a frame should be discarded, achieving an online implementation of the graph pruning algorithm for feature mapping, of which the pruned frame can be any of the preserved frames. The experimental results using real world datasets show that the proposed pruning algorithm can effectively reduce the size of the graph while maintaining the map accuracy.
Wijerathna, B.S., Vidal Calleja, T.A., Kodagoda, S., Zhang, Q. & Valls Miro, J. 2013, 'Multiple defect interpretation based on Gaussian processes for MFL technology', Proceedings of SPIE - The International Society for Optical Engineering vol 8694 - Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2013, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2013, SPIE, San Diego, USA, pp. 1-12.
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Magnetic Flux Leakage (MFL) technology has been used in non-destructive testing for more than three decades. There have been several publications in detecting and sizing defects on metal pipes using machine learning techniques. Most of these literature focus on isolated defects, which is far from the real scenario.
Xu, Z., Fitch, R., Sukkarieh, S. & IEEE 2013, 'Decentralised Coordination of Mobile Robots for Target Tracking with Learnt Utility Models', 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp. 2014-2020.
Yoo, C., Fitch, R. & Sukkarieh, S. 2013, 'Probabilistic temporal logic for motion planning with resource threshold constraints', Robotics: Science and Systems, pp. 457-464.
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© 2013 Massachusetts Institute of Technology. Temporal logic and model-checking are useful theoretical tools for specifying complex goals at the task level and formally verifying the performance of control policies. We are interested in tasks that involve constraints on real-valued energy resources. In particular, autonomous gliding aircraft gain energy in the form of altitude by exploiting wind currents and must maintain altitude within some range during motion planning. We propose an extension to probabilistic computation tree logic that expresses such real-valued resource threshold constraints, and present model-checking algorithms that evaluate a piecewise control policy with respect to a formal specification and hard or soft performance guarantees. We validate this approach through simulated examples of motion planning among obstacles for an autonomous thermal glider. Our results demonstrate probabilistic performance guarantees on the ability of the glider to complete its task, following a given piecewise control policy, without knowing the exact path of the glider in advance.
Yoo, C., Fitch, R., Sukkarieh, S. & IEEE 2013, 'Provably-Correct Stochastic Motion Planning with Safety Constraints', 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp. 981-986.
Zainudin, Z., Kodagoda, S. & Dissanayake, G. 2013, 'Mutual Information Based Data Selection in Gaussian Processes for 2D Laser Range Finder Based People Tracking', IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Wollongong, Australia, pp. 477-482.
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In general, a model to describe human motion patterns should have a capability of enhancing tracking performance even with long term occlusions. One way of effectively learn these patterns is to apply Gaussian Processes (GP). However, with the increase of the amount of training data with time, the GP becomes computationally expensive. In this work, we have proposed a Mutual Information (MI) based technique along with the Mahalanobis Distance (MD) measure to keep the most informative data while discarding the least informative data. The algorithm is tested with data collected in an office environment with a Segway robot equipped with a laser range finder. It leads to more than 90% data reduction while keeping the limit of Average Route Mean Square Error (ARMSE). We have also implemented a GP based Particle filter tracker for long term people tracking with occlusions. The comparison results with Extended Kalman Filter (EKF) based tracker shows the superiority of the proposed approach.
Zhao, L., Huang, S. & Dissanayake, G. 2013, 'Linear SLAM: A Linear Solution to the Feature-based and Pose Graph SLAM based on Submap Joining', 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Tokyo, Japan, pp. 24-30.
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This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.