Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments
Abstract Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles’ velocity using the randoml...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84821-2 |
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| author | Shiwei Lin Jianguo Wang Bomin Huang Xiaoying Kong Hongwu Yang |
| author_facet | Shiwei Lin Jianguo Wang Bomin Huang Xiaoying Kong Hongwu Yang |
| author_sort | Shiwei Lin |
| collection | DOAJ |
| description | Abstract Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles’ velocity using the randomly generated angles, which enhances the algorithm’s searchability and avoids premature convergence. It is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Transit Search (TS) algorithms by benchmark functions. It has great performance in unimodal optimization problems, and it gains the best fitness value with fewer iterations and average runtime than other algorithms. The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. The presented BPSO-RL algorithm combines the advantages of the swarm intelligence algorithm and the Q-learning method, which can generate the globally optimal path with fast computational speed and support in dealing with dynamic scenarios. It is validated through computational experiments with moving obstacles and compared with the PSO algorithm for AGV path planning. |
| format | Article |
| id | doaj-art-7eede911b53d4566b7853406628baa5c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7eede911b53d4566b7853406628baa5c2025-01-05T12:19:00ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84821-2Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environmentsShiwei Lin0Jianguo Wang1Bomin Huang2Xiaoying Kong3Hongwu Yang4School of Computer Engineering, Jimei UniversityFaculty of Engineering and Information Technology, University of Technology SydneySchool of Computer Engineering, Jimei UniversitySchool of IT and Engineering, Melbourne Institute of Technology (Sydney Campus)Xiamen Topstar Co., Ltd.Abstract Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles’ velocity using the randomly generated angles, which enhances the algorithm’s searchability and avoids premature convergence. It is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Transit Search (TS) algorithms by benchmark functions. It has great performance in unimodal optimization problems, and it gains the best fitness value with fewer iterations and average runtime than other algorithms. The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. The presented BPSO-RL algorithm combines the advantages of the swarm intelligence algorithm and the Q-learning method, which can generate the globally optimal path with fast computational speed and support in dealing with dynamic scenarios. It is validated through computational experiments with moving obstacles and compared with the PSO algorithm for AGV path planning.https://doi.org/10.1038/s41598-024-84821-2 |
| spellingShingle | Shiwei Lin Jianguo Wang Bomin Huang Xiaoying Kong Hongwu Yang Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments Scientific Reports |
| title | Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments |
| title_full | Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments |
| title_fullStr | Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments |
| title_full_unstemmed | Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments |
| title_short | Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments |
| title_sort | bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments |
| url | https://doi.org/10.1038/s41598-024-84821-2 |
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