Abstract
Rapidly-Exploring Random Trees (RRT) have been successfully used in motion planning problems involving a wide range of constraints. In this paper we develop a more robust and efficient version of the constrained RRT planning algorithm of [1]. The key idea is based on first constructing RRTs on tangent space approximations of constraint manifold, and performing lazy projections to the constraint manifold when the deviation exceeds a prescribed threshold. Our algorithm maintains the Voronoi bias property characteristic of RRT-based algorithms, while also reducing the number of projections. Preliminary results of a numerical study, together with a discussion of the potential strengths and weaknesses of our algorithm, are presented.
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Um, T.T., Kim, B., Suh, C., Park, F.C. (2010). Tangent Space RRT with Lazy Projection: An Efficient Planning Algorithm for Constrained Motions. In: Lenarcic, J., Stanisic, M. (eds) Advances in Robot Kinematics: Motion in Man and Machine. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9262-5_27
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DOI: https://doi.org/10.1007/978-90-481-9262-5_27
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Online ISBN: 978-90-481-9262-5
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