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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。
In order to solve the planning problem of UAV shortest coverage search path in irregular area, a new solution method is proposed. Firstly, the mission area is rasterized by using the detection range of airborne sensors, and the problem of area coverage search path planning is transformed into a solvable traveling salesman problem. Then, the genetic algorithm is improved by using multi-population parallel algorithm framework and elite strategy And re-design the fitness function of the algorithm, a parallel elitist genetic algorithm is proposed to solve the problem. Experimental results show that the proposed method has good applicability to the UAV area coverage search path planning problem. The proposed PEGA algorithm has a high convergence rate and a high quality of the optimal solution. It can effectively reduce the fitness function by improving the fitness function The case of long-distance two-point connection has obviously optimized the result of covering search path planning.