系统仿真学报 ›› 2026, Vol. 38 ›› Issue (6): 1647-1668.doi: 10.16182/j.issn1004731x.joss.25-0644

• 论文 • 上一篇    

改进多目标差分算法应用于机械臂多目标轨迹规划研究

刘满强, 尚自强   

  1. 兰州理工大学 自动化与电气工程学院,甘肃 兰州 730000
  • 收稿日期:2025-07-07 修回日期:2025-08-19 出版日期:2026-06-25 发布日期:2026-06-25
  • 通讯作者: 尚自强
  • 第一作者简介:刘满强(1970-),男,高工,硕士生导师,硕士,研究方向为有色冶金装备控制。
  • 基金资助:
    国家自然科学基金青年项目(62203196)

Application of Improved Multi-objective Differential Algorithm in Robotic Arm Multi-objective Trajectory Planning

Liu Manqiang, Shang Ziqiang   

  1. School of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730000, China
  • Received:2025-07-07 Revised:2025-08-19 Online:2026-06-25 Published:2026-06-25
  • Contact: Shang Ziqiang

摘要:

为解决单一目标的轨迹规划方法已难以满足机械臂精确性、多样性和复杂性的要求,提出一种基于改进多目标差分进化算法(guided multi-objective differential evolution, GMODE)的轨迹规划模型。利用三次多项式插值与B样条曲线构建多目标函数,并通过GMODE克服传统算法在种群多样性不足、易陷入局部最优和收敛缓慢等问题。引入分组策略、参数生成机制、基于模糊C均值聚类的精英变异优化B样条控制节点。仿真实验结果表明:该方法在时间、能量与冲击方面均取得优异表现;在结合自适应权重后,多目标性能得到明显提升。

关键词: 轨迹规划, 多目标优化, 机械臂, 多目标差分进化算法, 自适应权重

Abstract:

It is difficult for single-objective trajectory planning methods to meet the requirements of precision, diversity and complexity of robotic arms. A trajectory planning model based on an improved multi-objective differential evolution algorithm (guided multi-objective differential evolution, GMODE) algorithm is proposed. Cubic polynomial interpolation and B-spline curves are employed to construct multi-objective functions, while GMODE is adopted to overcome the limitations of traditional algorithms, such as insufficient population diversity, the tendency to fall into local optima, and slow convergence. A grouping strategy, parameter generation mechanism, and elite mutation based on fuzzy C-means clustering are introduced to optimize B-spline control nodes. Simulation experiments demonstrate that the proposed method achieves excellent performance in terms of time, energy, and impact. Furthermore, the incorporation of adaptive weights significantly enhances multi-objective performance.

Key words: trajectory planning, multi-objective optimization, robotic arm, multi-objective differential evolution algorithm, adaptive weights

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