基于改进模拟退火遗传算法的机械臂轨迹优化

doi:10.16182/j.issn1004731x.joss.23-1148

摘要/Abstract

摘要：

为了优化机械臂的工作轨迹，提出了一种改进模拟退火遗传算法。综合考虑机械臂的作业要求及性能特点，利用五次多项式插值的方法在关节空间内规划出一条平滑的运动轨迹。通过罚函数法处理不满足约束条件的个体，动态线性标定法对适应度函数进行重新标定。设置一种交叉概率和变异概率自适应调节机制改进遗传算法，并引入模拟退火算法的退火思想，有效避免了算法陷入局部最优。仿真结果表明：改进模拟退火遗传算法优化后的轨迹相比传统遗传算法有效缩短了机械臂的运动时间，进而提高了机械臂的工作效率。

关键词: 机械臂, 五次多项式插值, 模拟退火遗传算法, 轨迹优化, 罚函数法

Abstract:

To optimize the working trajectory of the robotic arm, a modified simulated annealing genetic algorithm is proposed. Comprehensively considering the operating requirements and performance characteristics of the robotic arm, the five-order polynomial interpolation method is used to plan a smooth motion trajectory in the joint space. The penalty function method is used to handle the individuals that do not meet the constraint conditions,and the fitness function is recalibrated by the dynamic linear calibration method.An adaptive adjustment mechanism for crossover probability and variation probability is set to modify the genetic algorithm. The cooling idea of the simulated annealing algorithm is introduced, which effectively avoids the algorithm falling into locally optimal. The results show that the optimized trajectory of the improved simulated annealing genetic algorithm effectively shortens the movement time of the robotic arm compared with the traditional genetic algorithm, and then improves the working efficiency of the robotic arm.

Key words: robotic arm, five-degree polynomial interpolation, simulated annealing genetic algorithms, trajectory optimization, penalty function method

中图分类号:

TP242

徐强,徐坚磊,胡燕海等 . 基于改进模拟退火遗传算法的机械臂轨迹优化[J]. 系统仿真学报, 2025, 37(2): 404-412.

Xu Qiang,Xu Jianlei,Hu Yanhai,et al . Trajectory Optimization of Robotic Arm Based on Improved Simulated Annealing Genetic Algorithm[J]. Journal of System Simulation, 2025, 37(2): 404-412.

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特性参数	尺寸/mm
机器人高度	1 328
机器人重量	1 582
重复定位精度	0.07
重复循环精度	0.20

关节i	α/(°)	a/mm	d/mm	θ/(°)
1	-90	150	445	θ₁
2	0	-700	0	θ₂
3	90	-115	0	θ₃
4	-90	0	795	θ₄
5	90	0	0	θ₅
6	0	0	85	θ₆

位置	关节1	关节2	关节3
起始点	0	0	0
固定点1	0.17	0.07	0.17
固定点2	0.35	0.10	0.44
固定点3	1.05	0.14	0.70
终止点	1.22	0.17	1.05

关节	角度/(°)	角速度/((°)/s)	角加速度/((°)/s²)
1	-180~180	75	130
2	-95~155	65	135
3	-180~95	80	155
4	-175~175	70	110
5	-120~120	55	105
6	-360~360	30	100

算法	关键点1	关键点2	关键点3	终止点	总计
初始值	2.5	2.5	2.5	2.5	10.0
传统遗传算法	2.30	1.73	1.97	2.36	8.36
改进自适应遗传算法	1.81	1.66	1.79	1.80	7.06
改进模拟退火遗传算法	1.62	1.54	1.63	1.74	6.53