Path planning model for UAV collaborative search task Based on NGA

To solve the problem of large search demand and insufficient search ability of UAVs, this article has proposed a task planning method of UAV group based on nested learning algorithm. According to the characteristics of the target path, the method combines task allocation strategy and path planning algorithm to make decision. This method can give reasonable suggestions on the number of UAVs, and effectively improve the efficiency of UAV group search task. The algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization efficiency. And the algorithm uses neighborhood method to avoid local optimum. Finally, the algorithm is verified by the data of a search task. The experimental results show that the planning method used in this paper is suitable for the UAVs' cooperative search problem. Compared with other planning methods, it has faster solution speed and is conducive to emergency decision-making. This method also has reference value for the cooperative planning of search tasks.