Volumetric Tree*: Adaptive Sparse Graph for Effective Exploration of Homotopy Classes

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We present volumetric tree*, a hybridization of sampling-based and optimization-based motion planning. Volumetric tree* constructs an adaptive sparse graph with volumetric vertices, hyper-spheres encoding free configurations, using a sampling-based motion planner for a homotopy exploration. The coarse-grained paths computed on the sparse graph are refined by optimization-based planning during the execution, while exploiting the probabilistic completeness of the sampling-based planning for the initial path generation. We also suggest a dropout technique probabilistically ensuring that the sampling-based planner is capable of identifying all possible homotopies of solution paths. We compare the proposed algorithm against the state-of-the-art planners in both synthetic and practical benchmarks with varying dimensions, and experimentally show the benefit of the proposed algorithm.
Publisher
IEEE Robotics and Automation Society / Robotics Society of Japan
Issue Date
2019-11-05
Language
English
Citation

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), pp.1496 - 1503

URI
http://hdl.handle.net/10203/268429
Appears in Collection
CS-Conference Papers(학술회의논문)
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