Multi-Robot Task Allocation in Agriculture Scenarios Based on the Improved NSGA-II Algorithm

Abstract

Agricultural multi-robot task allocation is an important direction of intelligent agricultural robots. In this paper, to ensure the shortest total movement distance of robots and balance the workload, we transform the task allocation of agricultural multi-robot into a multi objective multiple traveling salesman problem (MO-MTSP). A processing framework is also designed to adjust the allocation plan in real-time by monitoring the status of robots and tasks. In this processing framework, an improved Non-dominated genetic algorithm (INSGA-II) is utilized to achieve better performance of task allocation. The improvement includes: 1) An objective function is designed to ensure a balanced task workload distribution for each robot considering the actual conditions of the farmland; 2) A neighborhood search algorithm is used to accelerate the convergence of the algorithm by adjusting the initial population plan; 3) An adaptive adjustment mechanism for crossover and mutation probabilities is proposed to enhance the flexibility of the task allocation plan searching. Finally, the proposed algorithm is evaluated on both public datasets and actual agricultural plot datasets. Experimental results show that the improved algorithm can achieve better allocation results, demonstrating the practical applicability of the algorithm in task allocation of multiple robots employed in agriculture scenarios.