摘要
为解决无人机航迹规划中粒子群算法(PSO)易陷入局部极值和收敛速度慢的难题,提出了一种基于自适应柯西变异粒子群(ACMPSO)的无人机三维航迹规划算法。建立了无人机飞行三维环境模型;综合权衡航迹长度、雷达威胁、障碍物碰撞、高度变化等影响因素,构建了适应度函数;借助指数型惯性权重和柯西变异步长调节策略,探讨了迫使粒子跳出局部极值与加速算法收敛的方法;最后给出了复杂三维环境下的无人机航迹规划优化算法。以100 km×100 km×10 km任务空间的航迹规划为例,仿真结果验证了ACMPSO算法可有效弥补PSO的缺陷,所规划出的航迹可有效躲避障碍物和威胁,用时更少且品质更高。研究结果表明,采用ACMPSO算法在任务空间规划航迹是合理、可行和有效的。
In order to solve the puzzle that PSO is easy to fall into local extremum and slow convergence speed in UAV flight track planning,an ACMPSO optimization algorithm based on improved PSO was proposed.The three-dimensional environment model of UAV flight was established.Based on the comprehensive balance of track length,radar threat,obstacle collision,altitude change and other influencing factors,the fitness function was constructed.By means of exponential inertia weight and Cauchy variation step length regulation strategy,the method of forcing particles to jump out of local extremum and accelerating convergence algorithm was discussed.An algorithm for UAV trajectory planning optimization in complex 3D environment was presented.Taking the flight track planning of 100 km×100 km×10 km mission space as an example,the simulation results verify that ACMPSO algorithm can avoid the defects of PSO algorithm,and the planned flight path can effectively avoid obstacles and threats,take less time and have higher quality.The results show that the ACMPSO algorithm is reasonable,feasible and effective in mission space track planning.
作者
巫茜
罗金彪
顾晓群
曾青
WU Qian;LUO Jinbiao;GU Xiaoqun;ZENG Qing(College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China;Chongqing High-Tech Zone Feima Innovation Research Institute,Chongqing 400039,China)
出处
《兵器装备工程学报》
CSCD
北大核心
2021年第8期233-238,共6页
Journal of Ordnance Equipment Engineering
关键词
航迹规划
粒子群算法
柯西变异
无人机
适应度函数
track planning
particle swarm algorithm
Cauchy mutation
UAV
fitness function
