基于改进粒子群算法的工业机器人轨迹规划

Industrial robot trajectory planning based on improved PSO algorithm

针对传统工业机器人轨迹规划效率低、运行不稳定的问题,提出了可以动态调节学习因子的粒子群算法(PSO)。该方法通过分段多项式插值进行轨迹的拟合,并运用改进的粒子群算法以时间为适应度函数对工业机器人的轨迹进行优化,有效地将分段多项式插值函数与PSO相结合,避免了粒子群算法构造适应函数的复杂过程,对于传统的PSO初期较易落入局部极值且后期收敛速度缓慢的问题得到了改善。通过实验得到机械手每个关节的运动位姿、速度和加速度轨迹可知,该方法可以有效地实现工业机器人的轨迹优化,并且在提高运行效率的同时保证整体运行的稳定。

Aiming at the low efficiency and instability of traditional industrial robot trajectory planning,a particle swarm optimization algorithm(PSO)that can dynamically adjust learning factors is proposed.This method uses piecewise polynomial interpolation to fit the trajectory,and uses an improved particle swarm algorithm to optimize the trajectory of industrial robots with time as a fitness function.This method effectively combines the piecewise polynomial interpolation function with PSO,avoids the complex process of particle swarm algorithm to construct the adaptation function,and improves the problem that the traditional PSO is more likely to fall into the local extreme value in the early stage and the convergence speed is slow in the later stage.Through experiments,the motion posture,velocity and acceleration trajectories of each joint of the manipulator are known.This method can effectively achieve the optimization of the trajectory of the industrial robot,and ensure the stability of the overall operation while improving the operating efficiency.