Maximum entropy searching

CAAI Transactions on Intelligence Technology

Hui Zhou¹  Han Wang¹  Rui Jiang²  Shuzhi Sam Ge² 

[1] Nanyang Technological University;Qingdao University;

关键词: mobile robots;    path planning;    search problems;    sampling methods;    iterative methods;    entropy;    trees (mathematics);    ME-RRT;    2D/3D scenarios;    rapidly-exploring random tree planner;    time complexity;    trees;    goal-biased approach;    path integral approximation;    random path sampling;    searching direction;    random tree generation;    causal entropy maximisation;    biasing direction;    autonomous mobile robots path;   

DOI  :  10.1049/trit.2018.1058

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown