Maximum entropy searching. Issue 1 (20th February 2019)

• Record Type: Journal Article
• Authors: Jiang, Rui; Zhou, Hui; Wang, Han; Ge, Shuzhi Sam
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 1(2019)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-02-20
• Subjects: 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; C1140Z Other topics in statistics -- C1160 Combinatorial mathematics -- C3120C Spatial variables control -- C3390C Mobile robots; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1058
• Languages: English
• ISSNs: 2468-6557

Abstract

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.