Multi AGV Task Allocation Considering Load Balancing

Multi robot task allocation problem (MRTP) has important application value in the fields of intelligent manufacturing, warehousing logistics and unmanned distribution. The traditional genetic algorithm (GA) has the problems of unbalanced load and easy to fall into local optimization in task allocation. Therefore, this paper proposes an improved genetic algorithm to improve the load balance and overall performance of multi robot task allocation based on automated guided vehicle (AGV) by optimizing the fitness function and introducing adaptive crossover mutation strategy. The experimental results show that compared with the traditional GA, the improved genetic algorithm can better balance the load, reduce the task completion time, and improve the convergence speed of the algorithm.