基于栅格法的农业机器人路径规划方法研究

Research on Path Planning Method of Agricultural Robot Based on Grid Method

智能农业机器人是无人农场实现机器完全替换人工劳动的重要一环,而路径规划是农业机器人的重要关键技术。为此,针对农田特殊作业环境及农艺要求,提出了农业机器人全局路径规划方法和局部最优规划方法。阐述了农田全覆盖路径规划思想,设计了基于神经元激励网络法的全覆盖路径规划方法和基于Dijkstra算法的局部最优路径规划方法。通过MatLab仿真试验验证路径规划方法的可行性,建立栅格地图,并随机设置障碍物信息模拟实际作业环境。研究结果表明:生物激励与神经元激励两种规划方法覆盖完成率均为100%,路径长度与转弯次数两者相差不大,但神经元激励法的路径重复率5.49%远远小于生物激励法的12.66%;基于神经元激励网络法的全覆盖路径规划方法比生物激励网络法的全覆盖路径规划方法重耕率更低,可实现农田地块大规模覆盖作业;基于Dijkstra算法的局部最优路径规划方法可针对不同复杂程度的障碍物地图实现自主最优路径规划。

Intelligent agricultural robot is an important link to realize the complete replacement of manual labor by machines in unmanned farms,and path planning is an important key technology of agricultural robots.According to the special working environment of farmland and agronomic requirements,this paper proposes a global path planning method and a local optimal planning method for agricultural robots.The idea of full coverage path planning of farmland is expounded,and the full coverage path planning method based on neural network and the local optimal path planning method based on Dijkstra algorithm are designed.The feasibility of the path planning method is verified by MATLAB simulation test.The grid map is established,and the obstacle information is randomly set to simulate the actual working environment.The results show that the coverage completion rate of the two planning methods of biological excitation and neuron excitation is 100%,and the difference between the path length and the number of turns is small,However,the path repetition rate of the neuron excitation method is 5.49%,which is far less than that of the biological excitation method,which is 12.66%.The full coverage path planning method based on the neuron excitation network method has a lower re tillage rate than the full coverage path planning method based on the biological excitation network method,and can realize the large-scale coverage operation of farmland plots;The local optimal path planning method based on Dijkstra algorithm can realize autonomous optimal path planning for obstacle maps with different complexity.