Global Path Planning Algorithm for Unmanned Vehicles Based on Multi-Objective Particle Swarm Optimization Strategy

被引：0

作者：

Xie, Jun ^{[1
]}

Jiang, Qing ^{[2
]}

Wang, Yuxiao ^{[2
]}

Zhang, Yifan ^{[2
]}

机构：

[1] Nanchang Univ, Sch Math & Comp Sci, Nanchang 330031, Peoples R China

[2] West Anhui Univ, Fac Elect & Informat Engn, Luan 237012, Peoples R China

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2024年 / 38卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Global path planning; unmanned vehicle; multi-objective optimization; ROBUST;

D O I：

10.1142/S0218001424510121

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To overcome the limitations of unmanned vehicle global path planning with the sole objective of distance, a multi-objective particle swarm optimization strategy is proposed for indoor unmanned vehicle path planning. This strategy integrates objectives related to travel distance and cumulative turning angle. The traditional distance function is enhanced to accelerate algorithm convergence, and cumulative turning angle is introduced to construct a comprehensive function, meeting the demands of multi-objective navigation. The Pareto solution set concept is incorporated, and through the multi-objective particle swarm optimization algorithm, optimal paths for different travel objectives are identified, enhancing solution comprehensiveness. Experimental results validate the feasibility and effectiveness of the improved algorithm.

引用

页数：13

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