4.6 Article

A new approach to smooth path planning of mobile robot based on quartic Bezier transition curve and improved PSO algorithm

期刊

NEUROCOMPUTING
卷 473, 期 -, 页码 98-106

出版社

ELSEVIER
DOI: 10.1016/j.neucom.2021.12.016

关键词

Smooth path planning; Mobile robot; Quartic Bezier transition curve; Particle swarm optimization; PSO

资金

  1. National Natural Science Foundation of China [61703242]

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This paper proposes a new approach for smooth path planning of a mobile robot using a new quartic Bezier transition curve and an improved particle swarm optimization (PSO) algorithm. The quartic Bezier transition curve is constructed to ensure G3 continuity at the joints of the path segments, while the PSO algorithm is used to optimize the smooth path planning problem. Simulation experiments demonstrate the effectiveness and superiority of the proposed approach.
In this paper, a new approach is proposed for the smooth path planning of mobile robot based on a new quartic Bezier transition curve and an improved particle swarm optimization (PSO) algorithm. First, a dedicatedly constructed quartic Bezier transition curve with three overlapped control points is developed to fulfil the G3-continuity of the smooth path at the joints of the path segments, so as to guarantee the high-order smoothness of the path for the movement of mobile robot. Then, the smooth path planning of mobile robot is formulated mathematically as an optimization problem under several criteria and constraints of the smooth path, e.g. length, smoothness, safety and robot kinematics. Furthermore, an improved PSO with adaptive weighted delay velocity (PSO-AWDV) algorithm is presented for the optimization problem of smooth path planning, where the parameter relationship to ensure the convergence of PSO-AWDV is derived through the stability analysis of the algorithm. Finally, several simulation experiments are carried out to confirm the effectiveness and superiority of the proposed new approach combined with the new quartic Bezier transition curve and the improved PSO-AWDV algorithm. (c) 2021 Elsevier B.V. All rights reserved.

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