An efficient and global interactive optimization methodology for path planning with multiple routing constraints

Guo Xie; Xulong Du; Siyu Li; Jing Yang; Xinhong Hei; Tao Wen

doi:10.1016/j.isatra.2021.03.041

An efficient and global interactive optimization methodology for path planning with multiple routing constraints

ISA Trans. 2022 Feb:121:206-216. doi: 10.1016/j.isatra.2021.03.041. Epub 2021 Mar 29.

Authors

Guo Xie¹, Xulong Du², Siyu Li², Jing Yang³, Xinhong Hei², Tao Wen⁴

Affiliations

¹ Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China. Electronic address: guoxie@xaut.edu.cn.
² Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
³ Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; School of Mechatronics and Automotive Engineering, Tianshui Normal University, Tianshui 741000, China.
⁴ School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

PMID: 33867133
DOI: 10.1016/j.isatra.2021.03.041

Abstract

Path planning problem is attracting wide attention in autonomous system and process industry system. The existed research mainly focuses on finding the shortest path from the source vertex to the termination vertex under loose constraints of vertex and edge. However, in realistic, the constraints such as specified vertexes, specified paths, forbidden paths and forbidden vertexes have to be considered, which makes the existing algorithms inefficient even infeasible. Aiming at solving the problems of complex path planning with multiple routing constraints, this paper organizes transforms the constraints into appropriate mathematical analytic expressions. Then, in order to overcome the defects of existing coding and optimization algorithms, an adaptive strategy for the vertex priority is proposed in coding, and an efficient and global optimization methodology based on swarm intelligence algorithms is put forward, which can make full use of the high efficiency of the local optimization algorithm and the high search ability of the global optimization algorithm. Moreover, the optimal convergence condition of the methodology is proved theoretically. Finally, two experiments are inducted, and the results demonstrated its efficiency and superiority.

Keywords: Global optimization methodology; Multiple routing constraints; Path planning; Specified paths; Specified vertexes.