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<p>In this study of optimizing the dynamic characteristics of the manipulator, with the increase of motion redundancy, the complexity of solving the inverse motion problem increases significantly, making it extremely challenging to develop a general analytical solution algorithm. To address this challenge, this paper introduces a multi-strategy enhanced moth-flame optimization algorithm, DMFO. Firstly, the uneven initialization of MFO limits its comprehensive initial search capability and leads to slow convergence speed. Consequently, it lacks competitiveness in specific engineering problems. The mutation chaos strategy, quantum evolution strategy and precise elimination strategy are used to improve the formation of DMFO. Secondly, DMFO and eight meta-heuristic algorithms (GA, PSO, ABC, MFO, DE, WO, BKA, COA) are tested on CEC2017, demonstrating its superior local and global exploitation and exploration capabilities. Then these kinematics models of 4,6,7-DOF manipulators is established based on DH method, and the objective function is derived from the forward motion equation. The effectiveness of DMFO in solving the inverse kinematics of robot arms is validated through its application. Finally, the engineering verification is carried out on the manipulator UR16e, which highlights the success of DMFO in addressing the inverse motion problem of multi-DOF serial robots.</p>

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A multi-strategy enhanced moth-flame optimization algorithm for complex inverse kinematics problems in series robots

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Jianlin Liu,
  • Haisong Huang,
  • Qingsong Fan,
  • Chi Ma

摘要

In this study of optimizing the dynamic characteristics of the manipulator, with the increase of motion redundancy, the complexity of solving the inverse motion problem increases significantly, making it extremely challenging to develop a general analytical solution algorithm. To address this challenge, this paper introduces a multi-strategy enhanced moth-flame optimization algorithm, DMFO. Firstly, the uneven initialization of MFO limits its comprehensive initial search capability and leads to slow convergence speed. Consequently, it lacks competitiveness in specific engineering problems. The mutation chaos strategy, quantum evolution strategy and precise elimination strategy are used to improve the formation of DMFO. Secondly, DMFO and eight meta-heuristic algorithms (GA, PSO, ABC, MFO, DE, WO, BKA, COA) are tested on CEC2017, demonstrating its superior local and global exploitation and exploration capabilities. Then these kinematics models of 4,6,7-DOF manipulators is established based on DH method, and the objective function is derived from the forward motion equation. The effectiveness of DMFO in solving the inverse kinematics of robot arms is validated through its application. Finally, the engineering verification is carried out on the manipulator UR16e, which highlights the success of DMFO in addressing the inverse motion problem of multi-DOF serial robots.