采用新型蚁群算法的UAV动态航迹规划

Dynamic Route Planning for UAV Based on Novel Ant Colony Algorithm

航迹规划对UAV完成任务具有重要的意义。为解决突发威胁下的UAV航迹规划问题,提出了一种改进蚁群算法。采用全新的目标吸引策略、引入信息素增量调节因子并将优先级和信息素挥发系数进行组合优化来对基本蚁群算法进行了改进,提高了算法的求解效率,并进行仿真验证。根据战场已知威胁源生成Voronoi加权图,并与所提的改进蚁群算法相结合求解规划空间中的最优航迹。仿真结果表明,利用改进的蚁群算法能够有效地提高收敛速度和寻优能力,可以较好地解决突发威胁下的UAV航迹规划问题,保证UAV能够回避战场威胁,顺利飞抵目标点。

Route planning plays an important part in accomplishing task for UAV. Aiming at the route planning problem of UAV with unexpected threats, a method based on improved ant colony algorithm is proposed. In order to improve the solution efficiency of the algorithm, the new target attract strategy, the pheromone increment adjustment factor and the priority are introduced to improve the basic ant colony algorithm. Simulations are carried out. The weighted Voronoi diagram to search the optimal path speed and the ability in is created according to the certain threat sources, with the improved ant colony algorithm in the space. Simulation results show that the proposed method can improve the search searching the whole best solution of the route planning problem effectively, which is applicable to route planning of UAV with unexpected threats. So, the UAV can avoid the battlefield threats, reach the target point smoothly.