Multi-objective trajectory optimization method for manipulator based on improved sparrow search algorithm

doi:10.3785/j.issn.1006-754X.2025.05.111

Chinese Journal of Engineering Design

2025, Vol. 32

Issue (5): 664-674 DOI: 10.3785/j.issn.1006-754X.2025.05.111

Optimization Design

Multi-objective trajectory optimization method for manipulator based on improved sparrow search algorithm

Ling LI^1,²(

),Yulong HOU^1,²,Yao LI^1,²,Dan LUO¹,Miaoxia XIE¹

^1.School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
^2.Xi'an Key Laboratory of Intelligent Technology for Heavy Machinery Equipment, Xi'an 710311, China

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Abstract

Aiming at the problems such as low operational efficiency and mechanical fatigue damage caused by impact and vibration in traditional manipulators during task execution, a multi-objective trajectory optimization method based on an improved sparrow search algorithm (SSA) is proposed. Taking the six-degree-of-freedom AR4 manipulator as the research object, its kinematic model was constructed by using the segmented 3-5-3 polynomial interpolation method. Then, based on the newly improved SSA (NISSA) that integrated Tent-Logistic chaotic mapping, improved elite opposition-based learning strategy and Cauchy-Gaussian mutation strategy, the multi-objective collaborative optimization was carried out for the operation time and impact of each joint of the manipulator. Finally, comparative experiments were conducted with other optimization algorithms to verify the effectiveness of NISSA. The experimental results showed that after optimization with NISSA, the operation time of the manipulator was shortened by 17.8%, and the impact generated during operation was reduced by 12.9%. The research results provide an efficient method for the trajectory optimization of manipulators.

Key words： manipulator trajectory optimization sparrow search algorithm Tent-Logistic chaotic mapping elite opposition-based learning strategy

Received: 10 March 2025 Published: 31 October 2025

CLC:

TP 241.2

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Cite this article:

Ling LI,Yulong HOU,Yao LI,Dan LUO,Miaoxia XIE. Multi-objective trajectory optimization method for manipulator based on improved sparrow search algorithm. Chinese Journal of Engineering Design, 2025, 32(5): 664-674.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2025.05.111 OR https://www.zjujournals.com/gcsjxb/Y2025/V32/I5/664

基于改进麻雀搜索算法的机械臂多目标轨迹优化方法

针对传统机械臂在执行任务时存在工作效率低，以及易产生冲击和振动而造成机械疲劳损坏等问题，提出了一种基于改进麻雀搜索算法（sparrow search algorithm, SSA）的机械臂多目标轨迹优化方法。以六自由度AR4机械臂为研究对象，采用分段式3-5-3多项式插值法构建其运动学模型。然后，基于融合Tent-Logistic混沌映射、改良精英反向学习策略及柯西-高斯变异策略的新型改进SSA（newly improved SSA, NISSA），对机械臂各关节的运行时间和冲击进行多目标协同优化。最后，与其他优化算法进行对比实验，以验证NISSA的有效性。实验结果表明，应用NISSA优化后，机械臂的运行时间缩短了17.8%，运行中产生的冲击减小了12.9%。研究结果为机械臂的轨迹优化提供了高效的方法。

关键词： 机械臂, 轨迹优化, 麻雀搜索算法, Tent-Logistic混沌映射, 精英反向学习策略

Fig.1 Linkage coordinate system of manipulator

关节i	α_i_-1/(°)	a_i_-1/mm	d_i /mm	$θ i$ /(°)
1	-90	64.2	169.77	$θ 1$
2	0	305.0	0	$θ 2$
3	90	0	0	$θ 3$
4	-90	0	222.63	$θ 4$
5	90	0	0	$θ 5$
6	0	0	36.25	$θ 6$

Table 1 D-H parameters of manipulator

Fig.2 Frequency distribution histograms of each chaotic mapping

Fig.3 Comparison between Logistic chaotic mapping and Tent-Logistic chaotic mapping

Fig.4 Spatial position distribution of elite individuals

Fig.5 Flow of NISSA

Fig.6 Convergence curves of various algorithms under different test functions

Fig.7 Comparison of convergence curves of various algorithms during trajectory optimization of each joint in manipulator

Fig.8 Communication architecture of manipulator control system

Fig.9 Motion trajectory of manipulator

Table 2 Joint angles of manipulator

Fig.10 Comparison of operation time and impact of manipulator based on different algorithms

Fig.11 Curves of displacement, velocity and acceleration of each joint of optimized manipulator


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