Interval-based Global Localization in Building Maps

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A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application

We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.Comment: 2016 IEEE International Conference on Robotics and Automation (ICRA

Towards Robust Methods for Indoor Localization using Interval Data

International audienceIndoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than state-of-the-art methods

Cooperative tracking for persistent littoral undersea surveillance

Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 39-40).The US Navy has identified a need for an autonomous, persistent, forward deployed system to Detect, Classify, and Locate submarines. In this context, we investigate a novel method for multiple sensor platforms acting cooperatively to locate an uncooperative target. Conventional tracking methods based on techniques such as Kalman filtering or particle filters have been used with great success for tracking targets from a single manned platform; the application of these methods can be difficult for a cooperative tracking scenario with multiple unmanned platforms that have considerable navigation error. This motivates investigation of an alternative, set-based tracking algorithm, first proposed by Detweiler et al. for sensor network localization, to the cooperative tracking problem. The Detweiler algorithm is appealing for its conceptual simplicity and minimal assumptions about the target motion. The key idea of this approach is to compute the temporal evolution of potential target positions in terms of bounded regions that grow between measurements as the target moves and shrink when measurements do occur based on an assumed worst-case bound for uncertainty.(cont.) In this thesis, we adapt the Detweiler algorithm to the scenario of cooperative tracking for persistent undersea surveillance, and explore its limitations when applied to this domain. The algorithm has been fully implemented and tested both in simulation and with postprocessing of autonomous surface craft (ASC) data from the PLUSNet Monterey Bay 2006 experiment. The results indicate that the method provides disappointing performance when applied to this domain, especially in situations where communication links between the autonomous tracking platforms are poor. We conclude that the method is more appropriate for a "large N" tracking scenario, with a large number of small, expendable tracking nodes, instead of our intended scenario with a smaller number of more sophisticated mobile trackers. The method may also be useful as an adjunct to a conventional Bayesian tracker, to reject implausible target tracks and focus computational resources on regions where the target is present.by Robert Derek Scott.S.M.Nav.E

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics