基于改进的麻雀搜索算法的工业机器人轨迹优化

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-07-20 DOI:10.3390/machines12070490
Fei Ma, Weiwei Sun, Zhouxiang Jiang, Shuangfu Suo, Xiao Wang, Yue Liu
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引用次数: 0

摘要

本文提出了一种增强型多策略麻雀搜索算法,用于优化六轴工业机器人的运动轨迹,解决了机器人在运行过程中存在的效率低、关节振动大等问题。首先,采用改进的 D-H 参数法建立机器人的正向和逆向运动学模型。随后,对机器人采用 3-5-3 混合多项式插值轨迹规划方法。在传统麻雀算法的基础上,一个二维逻辑混沌系统对种群进行初始化。此外,还引入了利维飞行策略和非线性自适应加权来完善发现者位置更新算子,而反向学习策略则增强了守夜者位置更新算子。这些改进提高了算法的局部和全局搜索能力。然后,基于 3-5-3 混合多项式轨迹规划的改进型麻雀算法被用于机器人的时间最优轨迹规划。这与传统麻雀搜索算法和粒子群算法的优化结果进行了比较。研究结果表明,在机器人轨迹优化方面,所提出的增强型麻雀搜索算法在收敛速度和准确性上都优于标准麻雀算法和粒子群算法。这可以提高机器人的工作效率和性能。
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Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm
This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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