摘要
为解决不规则区域内UAV最短覆盖搜索路径的规划问题,提出一种新的求解方法。首先,利用机载传感器探测范围对任务区域进行栅格化离散,将区域覆盖搜索路径规划问题转化为一个可求解的旅行商问题;然后,利用多种群并行算法框架及精英策略对遗传算法进行改进并重新设计算法的适应度函数,提出一种并行精英遗传算法用于问题的求解。实验仿真结果表明,提出的求解方法对于UAV区域覆盖搜索路径规划问题具有较好的适用性;提出的PEGA算法收敛速度快,得出的最优解质量较高;通过改进适应度函数能够有效减少远距离两点相连的情况,对于覆盖搜索路径规划结果产生了明显的优化效果。
This paper proposes a new method for the shortest path planning of UAV coverage searching in irregular regions. First, the mission area was dispersed with rasterisation by using the detection range of airborne sensor, and the path planning problem of area coverage searching was translated into a travelling salesman problem that can be solved. Then, the genetic algorithm was improved by using the multi-group parallel algorithm frame and elitist strategy, and the fitness function of the algorithm was redesigned. The parallel elitist genetic algorithm was put forward to solve the TSP problem. Experimental results show that the proposed method is applicable to the path planning problem of UAV area coverage searching. The proposed PEGA algorithm had a high convergence speed and the optimal solution had satisfactory quality. Improving the fitness function reduced the case of long distance between two points connected, apparently optimizing the path planning results.
出处
《科技导报》
CAS
CSCD
北大核心
2014年第28期85-90,共6页
Science & Technology Review
基金
国防科技重点实验室基金项目(9140XXXXXXX1001)
关键词
无人机
并行精英遗传算法
区域覆盖搜索
路径规划
unmanned aerial vehicles
parallel elitist based on genetic algorithm
area coverage searching
path planning
