| This Paper:Browse 546 Download 0 |
码上扫一扫！ |
| Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach |
| LixinJia1,JinjunLi1,HongjieNi1,DanZhang1 |
|
|
| (1 College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China) |
|
| 摘要: |
|
| 关键词: |
| DOI：https://doi.org/10.1007/s11768-023-00139-w |
|
| 基金项目:This work was supported by the National Key R&D Funding of China (No. 2018YFB1403702) and the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars (No. LR22F030003). |
|
| Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach |
| Lixin Jia1,Jinjun Li1,Hongjie Ni1,Dan Zhang1 |
| (1 College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China) |
| Abstract: |
| 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. |
| Key words: Path planning · Autonomous mobile robot · Particle swarm optimization · Prior knowledge · Polynomial trajectory optimization |
