Please use this identifier to cite or link to this item: https://doi.org/10.1049/trit.2018.1058
Title: Maximum entropy searching
Authors: Jiang, R.
Zhou, H.
Wang, H.
Ge, S.S. 
Issue Date: 2019
Publisher: Institution of Engineering and Technology
Citation: Jiang, R., Zhou, H., Wang, H., Ge, S.S. (2019). Maximum entropy searching. CAAI Transactions on Intelligence Technology 4 (1) : 1-Aug. ScholarBank@NUS Repository. https://doi.org/10.1049/trit.2018.1058
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: This study presents a new perspective for autonomous mobile robots path searching by proposing a biasing direction towards causal entropy maximisation during random tree generation. Maximum entropy-biased rapidly-exploring random tree (ME-RRT) is proposed where the searching direction is computed from random path sampling and path integral approximation, and the direction is incorporated into the existing rapidly-exploring random tree (RRT) planner. Properties of ME-RRT including degenerating conditions and additional time complexity are also discussed. The performance of the proposed approach is studied, and the results are compared with conventional RRT/RRT* and goal-biased approach in 2D/3D scenarios. Simulations show that trees are generated efficiently with fewer iteration numbers, and the success rate within limited iterations has been greatly improved in complex environments. © 2018 IET. All Rights Reserved.
Source Title: CAAI Transactions on Intelligence Technology
URI: https://scholarbank.nus.edu.sg/handle/10635/209992
ISSN: 2468-6557
DOI: 10.1049/trit.2018.1058
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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