IEEE Transactions on Industrial Electronics-accepted version.pdf (10.98 MB)
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Informed anytime fast marching tree for asymptotically-optimal motion planning

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journal contribution
posted on 15.05.2020, 08:28 by Jing Xu, Kechen Song, Defu Zhang, Hongwen Dong, Yunhui Yan, Qinggang MengQinggang Meng
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.

Funding

National Natural Science Foundation of China (51805078, 51374063)

National Key Research and Development Program of China (2017YFB0304200)

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Industrial Electronics

Volume

68

Issue

6

Pages

5068 - 5077

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-05-12

Copyright date

2021

ISSN

0278-0046

eISSN

1557-9948

Language

en

Depositor

Prof Qinggang Meng . Deposit date: 14 May 2020