Abstract
Motion planning of self-reconfigurable robots in an environment is a challenging task. In this paper, we propose a sampling-based motion planning approach to plan locomotion of an organism with many degrees of freedom. The proposed approach is based on the Rapidly Exploring Random tree algorithm, which uses physical simulation to explore the configuration space of the highly articulated robots. Due to large number of actuators in such organisms, a novel randomized strategy for generating input signals is proposed. We demonstrate the performance of the proposed planner on a set of complex robots moving on a plane as well as on a rough surface.
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Vonásek, V., Košnar, K., Přeučil, L. (2012). Motion Planning of Self-reconfigurable Modular Robots Using Rapidly Exploring Random Trees. In: Herrmann, G., et al. Advances in Autonomous Robotics. TAROS 2012. Lecture Notes in Computer Science(), vol 7429. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32527-4_25
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DOI: https://doi.org/10.1007/978-3-642-32527-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32526-7
Online ISBN: 978-3-642-32527-4
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