基于改进粒子群算法的UAV航迹规划方法

结合当前无人机集群发展趋势,针对航迹规划算法和策略问题开展研究,在分析经典粒子群算法和传统航迹规划方法基础上,提出了一种基于改进粒子群算法的航迹规划方法,将无人机航迹规划分为整体航迹规划和节点间航迹规划两部分,针对两部分对于搜索速度和解的精度的不同需求,结合环境模型及约束条件,分别设计粒子群航迹规划算法的评价函数；对于节点间粒子群航迹规划,通过设计分段式惯性权重调整公式改进粒子群算法,在保证了算法的搜索速度的同时,提高了航迹规划解的精度。通过仿真验证了该方法的正确性和可行性,横向对比其他算法策略分析了该方法的优越性。最后在算法自主实时性方向上对于后续的工作开展提出了期望。

A Path Planning method of UAV Based on Improved PSO

Combined with the current trend of unmanned aerial vehicles(UAVs) development, with researching on path planning algorithm and strategy, on the basis of analyzing the classical particle swarm optimization(PSO) and the traditional path planning method, a path planning method based on improved PSO is proposed, dividing the UAVs path planning into two parts: overall path planning and inter-node path planning. According to the different requirements on search speed and accuracy of algorithm of the two parts, the evaluation function of PSO is designed by combining the environment model and constraint conditions. For inter-node path planning, the PSO is improved by designing a piecewise inertia weight adjustment formula, not only guarantees the search speed of the algorithm, but also improves the accuracy of the path planning solution. Simulation results show the correctness and feasibility of this method, and the superiority of this meth is analyzed by comparing with other algorithms. Finally the expectation of the following work is put forward in the aspect of autonomous real-time performance of algorithm.