Lin Shiwei, Wang Jianguo, Huang Bomin, Kong Xiaoying, Yang Hongwu
School of Computer Engineering, Jimei University, Xiamen, 361000, Fujian, China.
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, NSW, Australia.
Sci Rep. 2025 Jan 2;15(1):463. doi: 10.1038/s41598-024-84821-2.
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.
自动导引车在运输和工业环境中发挥着至关重要的作用。本文提出了一种用于全局路径规划的生物粒子群优化（BPSO）算法。BPSO算法修改了方程，使用随机生成的角度来更新粒子的速度，这增强了算法的搜索能力并避免了早熟收敛。通过基准函数将其与粒子群优化（PSO）、遗传算法（GA）和渡越搜索（TS）算法进行比较。它在单峰优化问题中具有出色的性能，并且与其他算法相比，在较少的迭代次数和平均运行时间内获得了最佳适应度值。采用Q学习方法进行局部路径规划以避开移动障碍物，并与所提出的BPSO相结合，以实现自动导引车的安全运行。所提出的BPSO-RL算法结合了群体智能算法和Q学习方法的优点，能够以快速的计算速度生成全局最优路径，并支持处理动态场景。通过带有移动障碍物的计算实验对其进行了验证，并与用于AGV路径规划的PSO算法进行了比较。
