三维信息素无人机平滑航迹规划算法

3D Pheromone UAV Smooth Path Planning Algorithm

小型固定翼无人机对三维空间的快速覆盖、连续巡逻以及航迹的平滑性,是无人机航迹规划急需解决的难点问题。在三维空间离散化基础上,提出了三维信息素无人机平滑航迹规划算法。该算法建立了一种信息素浓度动态变化模型,以信息素浓度嗅探引导无人机在三维空间循迹,并通过建立最大转向角准则平滑无人机飞行轨迹。本算法与高斯-马尔可夫移动模型算法、随机方向移动模型算法、基于覆盖的移动模型算法等的覆盖速率与巡逻能力进行仿真实验相比。仿真结果表明,本算法覆盖整个空间所用的时间为其他最快覆盖模型算法的61.4%,巡逻场景中维持高于其他模型算法15%的覆盖占比,且航迹平滑,不存在急转弯与急停。

The rapid coverage of 3D space,continuous patrol and track smoothness of small fixed-wing unmanned aerial vehicle(UAV)are difficult problems to be solved in UAV track planning.A 3D pheromone UAV smooth path planning algorithm is proposed,which is based on the discretization of 3D space.A dynamic change model of pheromone concentration is established.Pheromone concentration sniffing is used to guide the UAV to track in 3D space.The UAV flight trajectory is smoothed by establishing the maximum steering angle criterion.This algorithm is compared with Gauss-Markov mobility model algorithm,random direction mobility model algorithm,coverage-based mobility model algorithm,and other algorithms in the coverage rate and patrol ability.The time to cover the whole space of this algorithm is 61.4%of the other fastest coverage model algorithms through simulation.In the patrol scene,the coverage proportion is 15%higher than that of other model algorithms,and the track is smooth without sharp turns and sharp stops.