Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm

• Published in: Machines (Basel) Vol. 12; no. 7; p. 490
• Main Authors: Ma, Fei, Sun, Weiwei, Jiang, Zhouxiang, Suo, Shuangfu, Wang, Xiao, Liu, Yue
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Adaptive algorithms; Automation; Chaos theory; Energy consumption; improved sparrow search algorithm; Industrial production; industrial robot; Industrial robots; industry 4.0; Kinematics; Machine learning; Optimization algorithms; Optimization techniques; Particle swarm optimization; Polynomials; Robotics; Robots; Search algorithms; Strategy; Trajectory optimization; Trajectory planning
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines12070490

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.