Maximum entropy searching. Issue 1 (20th February 2019) - Jiang, Rui; Zhou, Hui; Wang, Han; Ge, Shuzhi Sam
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.