基于改进粒子群算法的无人机路径规划

UAV path planning based on improved particle swarm optimization

针对传统粒子群算法PSO求解无人机路径规划问题时存在极易陷入局部最优的问题,在PSO算法中引入细菌觅食算法BFO的趋化操作、迁徙操作,以提高其寻优能力。首先根据无人机飞行环境建立三维高程环境模型,并使用路径长度代价、障碍危险代价和航迹高程代价来构造适应度函数;然后在分析了粒子群算法和细菌觅食算法原理及特点的基础上,给出了算法的改进方法及其具体流程。最后,通过Matlab仿真验证表明:混合算法有效改善了粒子群算法的缺陷,在进行无人机路径规划时,相比于传统PSO算法,混合算法寻优精度和稳定性有明显改善。

Aiming at the problem that the traditional Particle Swarm Optimization(PSO)algorithm is easy to fall into the local optimum when it solves the UAV path planning problem,the chemotactic operation and migration operation of the Bacteria Foraging Algorithm(BFA)are introduced in the PSO algorithm to improve its optimization ability.Firstly,based on the UAV(Unmanned Aerial Vehicle)flight environment,a three-dimensional elevation environment model is established,and the fitness function is established by using the path length cost,the obstacle risk cost and the elevation cost.Se-condly,based on the analysis of the principles and characteristics of particle swarm algorithm and bacterial foraging algorithm,the improvement methods and specific procedures of the algorithm are given.Finally,the MATLAB simulation verification shows that the hybrid algorithm effectively improves the defects of the particle swarm optimization algorithm.Compared with the traditional PSO algorithm,the optimization accuracy and stability of the hybrid algorithm are significantly improved in UAV path planning.