一种复杂曲线插补轨迹的粒子群优化算法

Particle swarm optimization algorithm for complex curve interpolation trajectory

目前复杂曲线的插补主要有两种方式,通过B样条基函数参数的等距变化或对速度和加速度控制产生弦线逼近进行拟合,但这两种方式拟合精度不高.为了提高拟合精度,综合考虑了样条曲线局部特性,构建了误差增广目标函数,并采用粒子群算法对均匀三次B样条曲线插补轨迹寻求最优解.结果表明,算法可迅速收敛求出最优轨迹,且有效规避了局部最优带来的问题.

At present,there are two main ways of interpolating complex curves,which are fitted by the equidistant variation of the B-spline basis function parameters or the string approximation for velocity and acceleration control,but the fitting accuracy is not high.In order to improve the fitting precision,the local characteristics of the spline curve are considered comprehensively,and the error augmentation objective function is constructed.The particle swarm optimization algorithm is used to find the optimal solution for the uniform cubic B-spline curve interpolation trajectory.The results show that the algorithm can quickly converge to find the optimal trajectory,and effectively avoid the problems caused by local optimization.