Due to the increasing number of automated guided vehicles (AGVs) in the multi-AGV system and the limitation of working environment, path conflicts often occur in the working process of AGVs, which affects the working efficiency of the multi-AGV system. Thus, a optimization method by arranging the AGVs' traffic sequence is proposed in this paper. First, an AGV working map is reconstructed with graph theory, and then the corresponding collision avoidance rules are formulated for different types of conflicts. In multi-AGV system, each collision avoidance decision has an impact on the efficiency of the system, so it is crucial to adopt appropriate decisions. To optimize the decisions, the system fitness of different collision avoidance decisions are calculated based on the global state of the system, and the particle swarm optimization (PSO) algorithm is used to optimize the decisions. Furthermore, the PSO algorithm is improved by planning the direction of particle motion in the solution space and introducing mutation operation, so as to improve the search ability of the particle in the solution space. To verify the feasibility and effectiveness of the improved particle swarm optimization (IPSO) algorithm, an experiment system is built based on. NET platform. Results show that the IPSO algorithm than the traditional algorithms experimental performs better. The IPSO algorithm can effectively reduce congestion caused by path conflict and enhance the efficiency of the multi-AGV system.

中文翻译：

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混合路径下多AGV避碰全局优化方法研究

由于多AGV系统中自动导引车（AGV）数量的不断增加以及工作环境的限制，AGV在工作过程中经常发生路径冲突，影响多AGV系统的工作效率。因此，本文提出了一种通过排列AGV的交通序列来进行优化的方法。首先利用图论重构AGV工作图，然后针对不同类型的冲突制定相应的避碰规则。在多AGV系统中，每一次避碰决策都会对系统的效率产生影响，因此采取适当的决策至关重要。为了优化决策，基于系统的全局状态计算不同避碰决策的系统适应度，并且使用粒子群优化（PSO）算法来优化决策。进一步通过在解空间规划粒子运动方向并引入变异操作对PSO算法进行改进，提高粒子在解空间中的搜索能力。为验证改进粒子群优化(IPSO)算法的可行性和有效性，搭建了一个实验系统。网络平台。结果表明，IPSO算法比传统算法实验性能更好。IPSO算法可以有效减少路径冲突造成的拥塞，提升多AGV系统的效率。PSO算法通过在解空间中规划粒子运动方向并引入变异操作来改进粒子群算法，从而提高粒子在解空间中的搜索能力。为验证改进粒子群优化(IPSO)算法的可行性和有效性，搭建了一个实验系统。网络平台。结果表明，IPSO算法比传统算法实验性能更好。IPSO算法可以有效减少路径冲突造成的拥塞，提升多AGV系统的效率。PSO算法通过在解空间中规划粒子运动方向并引入变异操作来改进粒子群算法，从而提高粒子在解空间中的搜索能力。为验证改进粒子群优化(IPSO)算法的可行性和有效性，搭建了一个实验系统。网络平台。结果表明，IPSO算法比传统算法实验性能更好。IPSO算法可以有效减少路径冲突造成的拥塞，提升多AGV系统的效率。结果表明，IPSO算法比传统算法实验性能更好。IPSO算法可以有效减少路径冲突造成的拥塞，提升多AGV系统的效率。结果表明，IPSO算法比传统算法实验性能更好。IPSO算法可以有效减少路径冲突造成的拥塞，提升多AGV系统的效率。