首页|Autonomous mobile robot global path planning:a prior information-based particle swarm optimization approach

Autonomous mobile robot global path planning:a prior information-based particle swarm optimization approach

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The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.

Path planningAutonomous mobile robotParticle swarm optimizationPrior knowledgePolynomial trajectory optimization

Lixin Jia、Jinjun Li、Hongjie Ni、Dan Zhang

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College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,Zhejiang,China

National Key R&D Funding of ChinaZhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars

2018YFB1403702LR22F030003

2023

10.1007/s11768-023-00139-w

控制理论与技术(英文版)
华南理工大学

控制理论与技术(英文版)

CSCDEI
影响因子：0.307
ISSN：2095-6983
年,卷(期)：2023.21(2)
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