As the key intelligent equipment in the warehouse operation system, warehouse robots are indispensable in reducing logistics costs and improving logistics efficiency. Path planning is the core technology of warehouse robots, which directly affects the distance traveled by the robot during operation and is crucial for improving the efficiency of warehouse operation. Aiming at the problem of inefficient robot path planning in warehousing scenarios, this paper proposes a path planning model based on multi-robot paths and shortest as the objective function, with the number of robots and robot energy as the constraints. The model optimizes the robot path through an ant colony algorithm and subsequently uses the optimized path as the initial path for the genetic algorithm to improve the quality of the path. In this paper, the proposed algorithm is analyzed in comparison with a single ant colony algorithm and a single genetic algorithm in a warehousing scenario. Experimental results show that the proposed algorithm possesses better robustness and stability, and significantly improves the path planning efficiency of the warehouse robot.