基于改进粒子群算法的线缆路径规划方法研究

Research on Cable Path Planning Method Based on Improved PSO Algorithm

针对复杂机电产品布线路径规划过程中存在的效率较低、可应用性差等问题,提出一种改进粒子群算法,使用栅格法对布线空间进行划分,对障碍物建模并进行方向包围盒处理。为了避免算法在迭代过程中陷入局部最优,引入非线性逐渐递减的惯性权重与异步变化的学习因子,并且将贴壁约束加入到路径规划的过程中,保证线缆在敷设时路径的合理性。最后在仿真试验中,与标准粒子群算法进行对比,验证了改进后算法的合理性与可行性。

To address the problems of low efficiency and poor applicability in the process of wiring path planning for complex electromechanical products,an improved particle swarm optimization algorithm was proposed in which the raster method was used to partition the wiring space and model the obstacles.In order to avoid the algorithm falling into local optimality during iteration,non-linear gradually decreasing inertia weights with asynchronously varying learning factors were introduced,and wall-fitting constraints were added to the path planning process to ensure a reasonable path for the cables when they were laid.Finally,the improved algorithm was compared with the basic particle swarm optimization algorithm in simulation tests to verify the reasonableness and feasibility of the improved algorithm.