基于Grid-GSA算法的植保无人机路径规划方法  

Path Planning Method Based on Grid-GSA for Plant Protection UAV

作　　者：王宇[1] 陈海涛[1] 李煜[1] 李海川[1] 

机构地区：[1]东北农业大学工程学院,哈尔滨150030

出　　处：《农业机械学报》2017年第7期29-37,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：公益性行业(农业)科研专项(201303011);国家现代农业产业技术体系建设专项(CARS-04-PS22)

摘　　要：为了提高植保无人机的作业效率,研究了一种路径规划方法。运用栅格法构建环境模型,根据实际的作业区域规模、形状等环境信息和无人机航向,为相应栅格赋予概率,无人机优先选择概率高的栅格行进。基于上述机制实现了在形状不规则的作业区域内进行往复回转式全覆盖路径规划;以每次植保作业距离为变量,根据仿真算法得出返航点数量与位置来确定寻优模型中的变量维数范围,以往返飞行、电池更换与药剂装填等非植保作业耗费时间最短为目标函数,通过采用引力搜索算法,实现对返航点数量与位置的寻优;为无人机设置必要的路径纠偏与光顺机制,使无人机能够按既定路线与速度飞行。对提出的路径规划方法进行了实例检验,结果显示,相比于简单规划与未规划的情况,运用Grid-GSA规划方法得出的结果中往返飞行距离总和分别减少了14%与68%,非植保作业时间分别减少了21%与36%,其它各项指标也均有不同程度的提高。在验证测试试验中,实际的往返距离总和减少了322 m,实际路径与规划路径存在较小偏差。验证了路径规划方法具有合理性、可行性以及一定的实用性。Due to the limited battery power and pesticide capacity, the plant protection UAV need return to the supply point frequently in the process of plant protection. With the work area increasing, more time would be spent on battery replacement, pesticide filling and round trips between each return point and the supply point. So an appropriate path with the optimal return points must be planned before starting the work, in order to minimize the total time and improve the efficiency of the plant protection. For the purpose, a research was conducted on the path planning method for the plant protection UAV. Firstly, aiming at building an environment model which could describe the working area, the grid method was selected to divide the working area into small grids with the initialized weights, which were depended on the working area＇s size and shape. Secondly, the UAV was made to fly from the current grid to the adjacent one with the highest probability, which was calculated according to both the grids＇ initialized weights and the heading direction of the UAV. Incentive coefficients were added to the weights of the grids located in the front, left rear and right rear of the UAV so that the parallel routes were followed which moved from one extreme of the working area to the other alternately and turned at the boundary. Then the quantity and position of the return point could be outputted by controlling the distance in the spraying mode. Thirdly, a mathematical model was established. The quantities of the return times in the artificial planned path and the unplanned path were taken as the upper and lower limits of the search space respectively. The distance of each flight in the spraying mode was chosen as the variable, and the dimensions of which were depended on the search space. The objective was to obtain the optimal return points with the minimum time in the non-spraying mode. After that the gravitational search algorithm （GSA） was applied to solve the model. Based on the methods and processes above, a new p

关 键 词：植保无人机 路径规划 栅格法 返航点 引力搜索算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] V279.2[自动化与计算机技术—计算机科学与技术]