Reasonable task allocation not only improves the efficiency of task execution, but also reduces the total working time and energy consumption of the robot system. In this paper, an improved NSGA-II algorithm based on elite strategy is proposed for the multi-robot task allocation problem in orbital bolt operations. By combining elite selection, congestion ranking and adaptive cross-variance probability, this algorithm is able to achieve a better balance in multi-objective optimization. Experimental results show that the improved algorithm can significantly reduce the total distance traveled by the multi-robot system and effectively reduce the path deviation when dealing with different capacity datasets. For example, on the Kro_A100 dataset, the maximum path deviation is 0.14%, which is much lower than the traditional method. Through simulation experiments, when the algorithm runs in a space of 4000m×2000m, the path length of the shortest total time-consuming scheme is 42332.1 m, and the path length of the least power-consuming scheme is 32924.5 m. The results show that the improved NSGA-II algorithm not only improves the balanced robot path allocation, but also optimizes the task execution time and energy consumption. The method is highly scalable and applicable, and can provide an effective solution for practical multirobot task allocation problems.
