基于IKGC-PSO算法的无人机三维路径规划系统

UAV 3D Path Planning System Based on IKGC-PSO Algorithm

摘要:

为了解决标准粒子群算法在无人机三维路径规划中存在的易陷入局部最优、动态化不足和路径平滑性差等问题,提出了一种基于粒子群算法和遗传算法的,融入K均值精英化和柯西变异的优化算法；采用K均值聚类算法进行精英初始化,优化粒子种群的分布；动态化学习因子,强化惯性权重的全局性,保留粒子群算法收敛速度快的优点；融入遗传思想,采用柯西变异的方法,提高寻解最优解的能力；在对比实验中,模拟了实际的复杂三维环境,选取了路径总长度、飞行高度差以及马尔科夫生存状态组成目标函数；结果表明改进算法的鲁棒性提高了98%,求解质量相较于IPSO算法和PSO算法分别提高了5.8%和10.6%,验证了优化后方法的有效性和鲁棒性。

Abstract:

In order to solve the problems of the standard particle swarm algorithm in UAV 3D path planning, such as easy to fall into local optimum, insufficient dynamicization and poor path smoothing, an optimization algorithm based on particle swarm algorithm and genetic algorithm, incorporating K-mean elitization and Corsi variation, is proposed. In the comparison experiments, an actual complex 3D environment is simulated and the total path length, flight altitude difference and Markovian survival state are selected to form the objective function; the results show that the robustness of the improved algorithm is improved by 98% and the solution quality is higher than that of the IPSO algorithm and PSO algorithm by 5.8% and 10.6%, respectively, verifying the effectiveness and robustness of the optimized method.