【摘要】 为解决智慧无人农业中农业机器人对复杂农田的全区域覆盖作业问题，对农业机器人全区域作业覆盖进行研究，提出了一种基于人工势场改进的粒子群路径优化算法，并将其与回溯法相结合实现对农田的全区域路径覆盖。改进算法将人工势场中的势能场构建以及合力值动态调节方法引入粒子群算法，设置合力值调节因子来更新粒子群的速度更新函数，同时根据每个粒子的搜寻情况实时更新引力源参数值。与传统粒子群算法相比，改进粒子群算法寻找的最短路径减少10.6%,算法收敛速率提升12.8%;与传统算法与回溯法结合相比，改进粒子群算法与回溯法的结合使得全区域覆盖路径重复率减少了12.7%。更多还原

【Abstract】 In order to solve the problem of full-area coverage of complex farmland in the operation of intelligent agricultural unmanned machinery, the full-area path coverage of agricultural robots is studied. An improved particle swarm path optimization algorithm based on artificial potential field is proposed, which is combined with the backtracking method to realize full-area path coverage of farmland. The improved algorithm introduces the construction of potential energy field and the dynamic adjustment method of the resultant force value into the particle swarm algorithm, sets the resultant force regulator to update the velocity update function of the particle swarm, and updates the gravitational source parameter value in real time according to the search situation of each particle. Compared with the traditional particle swarm algorithm, the shortest path found by the improved particle swarm algorithm is shortened by 10.6%, and the convergence rate of the algorithm is increased by 12.8%. Compared with the combination of the traditional algorithm and the backtracking method, the combination of the improved algorithm and the backtracking method reduces the path repetition rate by 12.7%.更多还原