基于改进粒子群算法的航迹规划方法

Route Planning Based on Improved Particle Swarm Optimization Algorithm

摘要: 为了降低航迹规划的计算复杂度，航迹规划算法时常采用分层规划策略，在规划过程中分开处理不同性质的约束条件；分层规划包括外层规划和内层规划，内层规划是在外层规划的基础上进行的局部规划。本文提出了一种用于内层规划的改进粒子群算法，在粒子群算法中引入变异因子，设计了特定的扰动算子，提高了航迹寻优能力。仿真实验表明，在相同约束的规划环境中，改进方法较基本粒子群算法、基本遗传算法可以更快搜索到满足条件的航迹，提高了整体算法的规划速度。

Abstract: To reduce the computational complexity, route planning algorithms often use hierarchical planning strategy to deal with different constraints separately during the planning process. Hierarchical planning is divided into outer planning and inner planning. Inner planning is a local planning on the basis of outer planning. This paper proposes an improved particle swarm optimization algorithm for inner planning, introducing a variation factor into PSO algorithm. The improved algorithm designs a specific perturbation operator to enhance its search ability. The simulation results show that the improved method is more effective to get a satisfactory path in the same environment. It enhances the speed of the overall algorithm.