Research on Application of Multi-Robot Cooperative Scheduling Algorithm in Intelligent Warehouse

The application of multi-robot cooperative scheduling algorithm in intelligent warehouse solves the problems of path conflict between robots, uneven task allocation, poor scalability of the algorithm and real-time response to task changes. Through literature review and theoretical analysis, this paper combs the research status and development trend of multi-robot cooperative scheduling algorithm. In this study, a dynamic path planning strategy based on reinforcement learning (RL) is proposed. By defining state space, action space, reward function and strategy, the robot can dynamically adjust the path planning strategy according to real-time environmental information, effectively reducing path conflicts and obstacle avoidance time. The warehouse environment model constructed by simulation software is used to simulate the designed algorithm, and the performance is compared with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{*}$</tex> and Dijkstra algorithm. Experimental results show that RL algorithm is superior in reducing path length, times of obstacle avoidance and task completion time, and effectively reduces the incidence of path conflicts. This study verifies the effectiveness of RL algorithm in multi-robot cooperative scheduling of intelligent warehouse, and shows its potential in improving work efficiency and robustness.