关键词: 智能制造, 零件拣选, 深度强化学习, 多机器人路径规划, 注意力机制

Abstract: In intelligent manufacturing-oriented scenarios involving part-picking operations,the deployment of multiple robots in complex and dynamic environments significantly increases the likelihood of congestion and conflicts.Traditional path planning methods demonstrate inefficiencies in adapting to dynamic environmental changes and resolving path conflicts.To tackle this challenge,a conflict-free multi-robot path planning method based on Deep Reinforcement Learning（DRL）was proposed.A partially observable Markov decision process was utilized to model the robot path planning framework.The reward function was designed to incorporate potential energy guidance and area density rewards aimed at reducing congestion and conflicts.Then,an enhanced Multi-Agent Advantage Actor-Critic（MAA2C）algorithm for path planning was proposed,which improved information transfer and sharing among robots by introducing the attention mechanism,thereby algorithmic efficiency were enhanced.Simulation results of part picking in intelligent automobile assembly indicated that the proposed algorithm had the fastest convergence speed and the lowest total task picking time,and the superiority of the proposed model and algorithm was verified when applied to multi robot path planning in intelligent part-picking scenarios.

Key words: intelligent manufacturing, part-picking, deep reinforcement learning, multi-robot path planning, attention mechanism