Category Archives: Robotics

A survey of semantic mapping for mobile robots

March 11, 2015 11:11 ,

Ioannis Kostavelis, Antonios Gasteratos, 2015, Semantic mapping for mobile robotics tasks: A survey, Robotics and Autonomous Systems, Volume 66, April 2015, Pages 86-103, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.12.006.

The evolution of contemporary mobile robotics has given thrust to a series of additional conjunct technologies. Of such is the semantic mapping, which provides an abstraction of space and a means for human\u2013robot communication. The recent introduction and evolution of semantic mapping motivated this survey, in which an explicit analysis of the existing methods is sought. The several algorithms are categorized according to their primary characteristics, namely scalability, inference model, temporal coherence and topological map usage. The applications involving semantic maps are also outlined in the work at hand, emphasizing on human interaction, knowledge representation and planning. The existence of publicly available validation datasets and benchmarking, suitable for the evaluation of semantic mapping techniques is also discussed in detail. Last, an attempt to address open issues and questions is also made.

Posted in: Semantic maps for robots

Novel recursive bayesian estimator based on approaching pdfs by polynomials and keeping a hypothesis for each of its modes

February 10, 2015 20:08 ,

Huang, G.; Zhou, K.; Trawny, N.; Roumeliotis, S.I., (2015), A Bank of Maximum A Posteriori (MAP) Estimators for Target Tracking, Robotics, IEEE Transactions on , vol.31, no.1, pp.85,103. DOI: TRO.2014.2378432

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Nonlinear estimation problems, such as range-only and bearing-only target tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions. In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides relinearization of the entire state trajectory, multihypothesis tracking, and an efficient hypothesis generation scheme. Each estimator in the bank is initialized using a locally optimal state estimate for the current time step. Every time a new measurement becomes available, we relax the original batch-MAP problem and solve it incrementally. More specifically, we convert the relaxed one-step-ahead cost function into polynomial or rational form and compute all the local minima analytically. These local minima generate highly probable hypotheses for the target’s trajectory and hence greatly improve the quality of the overall MAP estimate. Additionally, pruning of least probable hypotheses and marginalization of old states are employed to control the computational cost. Monte Carlo simulation and real-world experimental results show that the proposed approach significantly outperforms the standard EKF, the batch-MAP estimator, and the particle filter.

Posted in: Mobile Robot Localization , Tagged: ,

Mental imaginery for a mobile robot that learns obstacle avoidance

February 10, 2015 16:58 ,

Wilmer Gaona, Esaú Escobar, Jorge Hermosillo, Bruno Lara (2015), Anticipation by multi-modal association through an artificial mental imagery process, Connection Science, 27:1, 68-88, DOI: 10.1080/09540091.2014.95628

Mental imagery has become a central issue in research laboratories seeking to emulate basic cognitive abilities in artificial agents. In this work, we propose a computational model to produce an anticipatory behaviour by means of a multi-modal off-line hebbian association. Unlike the current state of the art, we propose to apply hebbian learning during an internal sensorimotor simulation, emulating a process of mental imagery. We associate visual and tactile stimuli re-enacted by a long-term predictive simulation chain motivated by covert actions. As a result, we obtain a neural network which provides a robot with a mechanism to produce a visually conditioned obstacle avoidance behaviour. We developed our system in a physical Pioneer 3-DX robot and realised two experiments. In the first experiment we test our model on one individual navigating in two different mazes. In the second experiment we assess the robustness of the model by testing in a single environment five individuals trained under different conditions. We believe that our work offers an underpinning mechanism in cognitive robotics for the study of motor control strategies based on internal simulations. These strategies can be seen analogous to the mental imagery process known in humans, opening thus interesting pathways to the construction of upper-level grounded cognitive abilities.

Posted in: Developmental robotics , Tagged: ,

Active exploration strategy for RL in robots, and approximation of value function by Gaussian processes

February 10, 2015 16:49 ,

Jen Jen Chung, Nicholas R.J. Lawrance, Salah Sukkarieh (2015), Learning to soar: Resource-constrained exploration in reinforcement learning, The International Journal of Robotics Research vol. 34, pp. 158-172. DOI: 10.1177/0278364914553683

This paper examines temporal difference reinforcement learning with adaptive and directed exploration for resource-limited missions. The scenario considered is that of an unpowered aerial glider learning to perform energy-gaining flight trajectories in a thermal updraft. The presented algorithm, eGP-SARSA(\u03bb), uses a Gaussian process regression model to estimate the value function in a reinforcement learning framework. The Gaussian process also provides a variance on these estimates that is used to measure the contribution of future observations to the Gaussian process value function model in terms of information gain. To avoid myopic exploration we developed a resource-weighted objective function that combines an estimate of the future information gain using an action rollout with the estimated value function to generate directed explorative action sequences. A number of modifications and computational speed-ups to the algorithm are presented along with a standard GP-SARSA(\u03bb) implementation with Formula -greedy exploration to compare the respective learning performances. The results show that under this objective function, the learning agent is able to continue exploring for better state-action trajectories when platform energy is high and follow conservative energy-gaining trajectories when platform energy is low.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,

Solving the problem of the slow learning rate of reinfocerment learning through the acquisition of the transition model from the data

January 23, 2015 18:03 ,

Deisenroth, M.P.; Fox, D.; Rasmussen, C.E., Gaussian Processes for Data-Efficient Learning in Robotics and Control, Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.2, pp.408,423, Feb. 2015, DOI: 10.1109/TPAMI.2013.218

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Mathematical model of quartz crystal clocks and Kalman Filter estimation for clock synchronization

January 23, 2015 17:46 ,

Giorgi, G., An Event-Based Kalman Filter for Clock Synchronization, Instrumentation and Measurement, IEEE Transactions on , vol.64, no.2, pp.449,457, Feb. 2015, DOI: 10.1109/TIM.2014.2340631

The distribution of a time reference has long been a significant research topic in measurement and different solutions have been proposed over the years. In this context, the design of servo clocks plays an important role to get better performances by smoothing the influence of noise sources affecting a synchronization system. A servo clock is asked to provide an adaptive and conservative measure of the time distance between the local clock and the time reference by minimizing, if possible, the energy consumption. In this paper, we propose a servo clock based on an efficient implementation of the Kalman filter (KF), called in the following event-based KF that allows to overcome drawbacks of existing KF-based servo clocks with furthermore a significant reduction of the computational cost. An in-depth analysis of the synchronization uncertainty has been reported to completely characterize the proposed solution; and finally, some guidelines on how to correctly initialize the KF are provided.

Posted in: Networked telerobots , Tagged: ,

A new simple method for mobile robot path planning based on particles and inspired in bacteria

December 19, 2014 10:24 ,

Md. Arafat Hossain, Israt Ferdous, Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique, Robotics and Autonomous Systems, Volume 64, February 2015, Pages 137-141, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.07.002

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Path planning is one of the basic and interesting functions for a mobile robot. This paper explores the application of Bacterial Foraging Optimization to the problem of mobile robot navigation to determine the shortest feasible path to move from any current position to the target position in an unknown environment with moving obstacles. It develops a new algorithm based on Bacterial Foraging Optimization (BFO) technique. This algorithm finds a path towards the target and avoiding the obstacles using particles which are randomly distributed on a circle around a robot. The criterion on which it selects the best particle is the distance to the target and the Gaussian cost function of the particle. Then, a high level decision strategy is used for the selection and thus proceeds for the result. It works on local environment by using a simple robot sensor. So, it is free from having generated additional map which adds cost. Furthermore, it can be implemented without requirement to tuning algorithm and complex calculation. To simulate the algorithm, the program is written in C language and the environment is created by OpenGL. To test the efficiency of the proposed technique, results are compared with Basic Bacterial Foraging Optimization (BFO) and another well-known algorithm called Particle Swarm Optimization (PSO) to give the guarantee that the proposed method gives better and optimal path.

Posted in: Robot motion planning , Tagged: ,

Taking into account the way a path serves to avoid obstacles in order to improve the three main methods of robot path planning: graph-search, probabilistic and bug

December 19, 2014 10:17 ,

Emili Hernandez, Marc Carreras, Pere Ridao, A comparison of homotopic path planning algorithms for robotic applications , Robotics and Autonomous Systems, Volume 64, February 2015, Pages 44-58, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.10.021

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This paper addresses the path planning problem for robotic applications using homotopy classes. These classes provide a topological description of how paths avoid obstacles, which is an added value to the path planning problem. Homotopy classes are generated and sorted according to a lower bound heuristic estimator using a method we developed. Then, the classes are used to constrain and guide path planning algorithms. Three different path planners are presented and compared: a graph-search algorithm called Homotopic A∗ (HA∗), a probabilistic sample-based algorithm called Homotopic RRT (HRRT), and a bug-based algorithm called Homotopic Bug (HBug). Our method has been tested in simulation and in an underwater bathymetric map to compute the trajectory of an Autonomous Underwater Vehicle (AUV). A comparison with well-known path planning algorithms has also been included. Results show that our homotopic path planners improve the quality of the solutions of their respective non-homotopic versions with similar computation time while keeping the topological constraints.

Posted in: Robot motion planning , Tagged:

A survey on topological localization and mapping

December 19, 2014 10:13 ,

Emilio Garcia-Fidalgo, Alberto Ortiz, Vision-based topological mapping and localization methods: A survey , Robotics and Autonomous Systems, Volume 64, February 2015, Pages 1-20, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.11.009

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Topological maps model the environment as a graph, where nodes are distinctive places of the environment and edges indicate topological relationships between them. They represent an interesting alternative to the classic metric maps, due to their simplicity and storage needs, what has made topological mapping and localization an active research area. The different solutions that have been proposed during years have been designed around several kinds of sensors. However, in the last decades, vision approaches have emerged because of the technology improvements and the amount of useful information that a camera can provide. In this paper, we review the main solutions presented in the last fifteen years, and classify them in accordance to the kind of image descriptor employed. Advantages and disadvantages of each approach are thoroughly reviewed and discussed.

Posted in: Mobile robot SLAM , Tagged: , ,

Estimating the bandwidth of a communication channel for adjusting the bitrate in high-definition video streaming, using Pareto and Gamma distributions (that are conjugate) in a bayesian estimation framework

December 9, 2014 17:48 ,

Javadtalab, A.; Semsarzadeh, M.; Khanchi, A.; Shirmohammadi, S.; Yassine, A., Continuous One-Way Detection of Available Bandwidth Changes for Video Streaming Over Best-Effort Networks, Instrumentation and Measurement, IEEE Transactions on , vol.64, no.1, pp.190,203, Jan. 2015. DOI: 10.1109/TIM.2014.2331423

Video streaming over best-effort networks, such as the Internet, is now a significant application used by most Internet users. However, best-effort networks are characterized by dynamic and unpredictable changes in the available bandwidth, which adversely affect the quality of video. As such, it is important to have real-time detection mechanisms of bandwidth changes to ensure that video is adapted to the available bandwidth and transmitted at the highest quality. In this paper, we propose a Bayesian instantaneous end-to-end bandwidth change prediction model and method to detect and predict one-way bandwidth changes at the receiver. Unlike existing congestion detection mechanisms, which use network parameters such as packet loss probability, round trip time (RTT), or jitter, our approach uses weighted interarrival time of video packets at the receiver side. Furthermore, our approach is continuous, since it measures available bandwidth changes with each incoming video packet, and therefore detects congestion occurrence in <200 ms, on average, which is significantly faster than existing approaches. In addition, it is a one-way scheme, since it only takes into account the characteristics of the incoming path and not the outgoing path, as opposed to other approaches, which use RTT and are hence less accurate. In this paper, we provide extensive experimental simulations and real-world network implementation. Our results indicate that the proposed detection method is superior to existing solutions.

Posted in: QoS in networked telerobots , Tagged: , ,