2134/12302240.v1
Jing Xu
Jing
Xu
Kechen Song
Kechen
Song
Defu Zhang
Defu
Zhang
Hongwen Dong
Hongwen
Dong
Yunhui Yan
Yunhui
Yan
Qinggang Meng
Qinggang
Meng
Informed anytime fast marching tree for asymptotically-optimal motion planning
Loughborough University
2020
Electrical & Electronic Engineering
Information and Computing Sciences
Engineering
Asymptotic optimality
fast marching tree
informed anytime algorithm
motion planning
2020-05-15 08:28:49
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Informed_anytime_fast_marching_tree_for_asymptotically-optimal_motion_planning/12302240
In many applications, it is necessary for motion planning planners to get high-quality solutions in high-dimensional complex problems. In this paper, we propose an anytime asymptotically-optimal sampling-based algorithm, namely Informed Anytime Fast Marching Tree (IAFMT*), designed for solving motion planning problems. Employing a hybrid incremental search and a dynamic optimal search, the IAFMT* fast finds a feasible solution, if time permits, it can efficiently improve the solution toward the optimal solution. This paper also presents the theoretical analysis of probabilistic completeness, asymptotic optimality, and computational complexity on the proposed algorithm. Its ability to converge to a high-quality solution with the efficiency, stability, and self-adaptability has been tested by challenging simulations and a humanoid mobile robot.