机械臂多目标最优轨迹的自适应惩罚MOEA/D规划

Manipulator Multi-Object Optimal Trajectory Planning Based on Adaptive Penalty MOEA/D Algorithm

为了减少机械臂的工作时间和工作过程中的冲击量,提出了基于自适应惩罚MOEA/D算法的时间-冲击多目标规划方法。介绍了PUMA560机械臂结构,并建立了机械臂的连杆坐标系。建立了以减小机械臂工作时间和冲击为综合目标的优化模型,并分析了5次多项式插值函数。通过编码和建立适应度函数,将轨迹规划问题转化为最优基因搜索问题。根据邻域内解的密度设置了自适应惩罚因子,提出了机械臂轨迹的自适应惩罚MOEA/D规划方法。经仿真验证,自适应惩罚MOEA/D算法搜索的Pareto前沿解质量高于标准MOEA/D算法、文献[10]新型MOEA/D算法。且经过优化,机械臂工作时间减少了7.68%,冲击减少了17.32%。实验结果表明,自适应惩罚MOEA/D算法在机械臂轨迹规划中具有优越性。

In order to reduce the working time and impact of the manipulator,a time-impact multi-objective programming method based on adaptive penalty MOEA/D algorithm is proposed.The structure of PUMA560 manipulator is introduced,and the linkage coordinate system of the manipulator is established.An optimization model is established to reduce the working time and impact of the manipulator,and the quintic polynomial interpolation function is analyzed.By coding and establishing the fit-ness function,the trajectory planning problem is transformed into the optimal gene search problem.The adaptive penalty factor is set according to the density of the solution in the neighborhood,and the adaptive penalty MOEA/D planning method of the ma-nipulator trajectory is proposed.The simulation results show that the Pareto frontier solution quality searched by the adaptive pen-alty MOEA/D algorithm is higher than that of the standard MOEA/D algorithm and the new MOEA/D algorithm in reference[10].After optimization,the working time of the manipulator is reduced by 7.68%and the impact is reduced by 17.32%.The experi-mental results show that the adaptive penalty MOEA/D algorithm has advantages in manipulator trajectory planning.