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) : 1-8. 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 |
Appears in Collections: | Staff Publications Elements |
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