针对运动目标的多无人机协同鸽群优化搜索方法

针对多无人机协同运动目标搜索问题,本文设计了改进鸽群优化算法的协同搜索决策.首先,基于运动目标的独立性,建立了服从正态分布的目标概率信息图模型;为了提高环境中目标存在的确定度,建立了搜索环境的确定度信息图.其次,通过建立的吸引和排斥数字信息素图,引导无人机向未搜索区域飞行,减少重复搜索概率,提高协同目标搜索效率,并基于传统的鸽群算法,通过加入速度更新修正机制和精英代机制对其进行改进.然后,结合环境中目标的存在概率信息以及无人机搜索目标的探测信息,使用改进鸽群优化算法,规划无人机的最优搜索飞行路径.并设计避碰机制,以有效防止无人机搜索过程中的碰撞.最后,通过比较仿真实验验证了改进鸽群优化算法对运动目标协同搜索的有效性.

Multi-UAV cooperative pigeon-inspired optimization search method for moving targets

Aiming at the problem of multi-UAV cooperative moving target search, a cooperative search algorithm based on the improved pigeon-inspired optimization (IPIO) is designed. Firstly, based on the independence of moving targets, a target probability information graph model with normal distribution is established. In order to enhance the certainty of the presence of the targets in the environment, the information graph of search environment certainty is established. Secondly, in order to reduce the probability of repeated search and improve the efficiency of collaborative target search, the attractive and repulsive digital pheromone graphs are established to guide the UAVs to fly to the unsearched area. Based on the conventional pigeon-inspired optimization, the IPIO is designed by adding speed update and correction mechanism and elite generation mechanism. Then, combining with the existence probability information of the targets in the environment and the detection information of UAVs, the IPIO algorithm is used to determine the optimal searching flight paths for UAVs. And then, a collision avoidance strategy is designed to prevent the collision between UAVs in the searching process. Finally, the effectiveness of the present cooperative moving target search algorithm is verfied by comparative simulation experiments.