摘要
在无人机航迹规划效率优化问题的研究中,针对采用粒子群算法的无人机航迹规划后期,算法收敛速度减慢、航迹规划效率低的问题,提出了一种改进粒子群算法的无人机航迹规划方法。在航迹规划过程中,建立粒子浓度机制,对陷入局部最优的粒子群进行粒子浓度分析,结合粒子的适应度构造粒子平衡算子,对解空间中适应度大、浓度低的粒子进行相应的变异,促使局部最优解快速跳出局部极值,加快收敛速度,提高规划效率。通过仿真结果验证了改进粒子群算法在无人机三维航迹规划中的有效性。
The basic PSO algorithm in the optimization process of UAV route planning, at the last stage of the al- gorithm, can reduce the efficiency of route planning. To overcome this problem, we proposed a methord for UAV route planning based on an an improved particle swarm optimization algorithm. In the route planning, we established a particle concentration mechanism, analysed the concentration of the particle swarm which constructed a particle bal- ance operator in the local optimum combined with the particles fitness, gave corresponding variation to those particles which have better fitness and lower concentration, and prompted local optimal solution to jump out of the local ex- treme points quickly, to find the optimal route in a more rapid convergence speed. The simulation results show the ef- fectiveness of the improved PSO algorithm in the 3D route planning of UAV.
出处
《计算机仿真》
CSCD
北大核心
2014年第3期65-69,共5页
Computer Simulation
关键词
无人机
航迹规划
粒子群算法
适应度函数
UAV
Route planning
Particle swarm algorithm
Fitness function
