Abstract:The order task allocation of autonomous mobile swarm robots in intelligent warehousing is modeled as a multi-objective optimization problem of cooperative swarm robotic scheduling, in which the path and time cost of member robots completing the picking task is viewed as the optimization objective. An ant colony-genetic algorithm fusion framework is designed. In this framework, the ant colony algorithm is taken as the secondary algorithm for initial population optimization, while the improved genetic algorithm as the main. To be specific, an elite reservation strategy is adopted after the roulette wheel selection operator in the genetic algorithm, and the inversion operator is added. A series of task allocation experiments are performed under conditions of different numbers of tasks and swarm sizes. The simulation results show that the proposed algorithm dominates over the ant colony algorithm and the genetic algorithm in performance. It combines the robustness of the ant colony algorithm and the global search ability of the genetic algorithm, improving the overall operation efficiency of the intelligent warehousing system.