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Optimization Methods for Target Tracking by Multi-robot Systems

  • Gerasimos G. Rigatos
Part of the Intelligent Systems Reference Library book series (ISRL, volume 7)

Abstract

The chapter studies the two-fold optimization problem of distributed motion planning and distributed filtering for multi-robot systems. Tracking of a target by a multi-robot system is pursued assuming that the target’s state vector is not directly measurable and has to be estimated by distributed filtering based on the target’s cartesian coordinates and bearing measurements obtained by the individual mobile robots. The robots have to converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in the motion plane. To solve the overall problem, the following steps are followed: (i) distributed filtering, so as to obtain an accurate estimation of the target’s state vector. This estimate provides the desirable state vector to be tracked by each one of the mobile robots, (ii) motion planning and control that enables convergence of the vehicles to the goal position and also maintains the cohesion of the vehicles swarm. The efficiency of the proposed distributed filtering and distributed motion planning scheme is tested through simulation experiments.

Keywords

Mobile Robot Extend Kalman Filter Reference Surface Mobile Sensor Unscented Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gerasimos G. Rigatos

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