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With the continuous advancement of automation technology, six-degree-of-freedom robotic arms are becoming more and more widely used in manufacturing, medical care, logistics, and services. Thanks to their flexibility and efficiency, these robots are gradually replacing traditional manual operations, improving production efficiency and reducing labour costs. However, how to achieve efficient path planning and joint angle optimisation for robots in complex and ever-changing environments remains a major challenge. To address this problem, this paper proposes a comprehensive application solution that combines the A algorithm with the simulated annealing algorithm. The A algorithm uses a heuristic search method that can quickly find the optimal path in a large state space, making it particularly suitable for multi-objective path planning in dynamic environments. The algorithm improves the speed and accuracy of path planning by comprehensively considering actual and heuristic costs. It is highly adaptable and can be adjusted in real time to meet different task requirements. The simulated annealing algorithm, as a global optimization tool, can effectively avoid local optima and help achieve an optimal configuration of joint angles. The algorithm performs well in high-dimensional optimization problems, improves the movement accuracy and efficiency of the robotic arm, and reduces energy consumption. Experimental results show that the path planning and joint angle optimisation strategy combining A* and simulated annealing algorithms can effectively cope with the challenges in complex environments and improve the overall performance of the robotic arm. The research results of this paper provide theoretical support and practical guidance for the application of intelligent robots in complex environments, and have important academic and practical value. Future research can explore the combined application of other optimisation algorithms to further enhance the adaptability of robotic arms in a wider range of scenarios, so as to promote the sustainable development and innovation of robotics.

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Research on Robot Path and Joint Angle Optimisation Based on the A* and Simulated Annealing Algorithms

  • 会议论文

  • 发表日期：

    2026年

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  • Yong Tian,
  • Guona Chen,
  • Yongjie Chen

摘要

With the continuous advancement of automation technology, six-degree-of-freedom robotic arms are becoming more and more widely used in manufacturing, medical care, logistics, and services. Thanks to their flexibility and efficiency, these robots are gradually replacing traditional manual operations, improving production efficiency and reducing labour costs. However, how to achieve efficient path planning and joint angle optimisation for robots in complex and ever-changing environments remains a major challenge. To address this problem, this paper proposes a comprehensive application solution that combines the A algorithm with the simulated annealing algorithm. The A algorithm uses a heuristic search method that can quickly find the optimal path in a large state space, making it particularly suitable for multi-objective path planning in dynamic environments. The algorithm improves the speed and accuracy of path planning by comprehensively considering actual and heuristic costs. It is highly adaptable and can be adjusted in real time to meet different task requirements. The simulated annealing algorithm, as a global optimization tool, can effectively avoid local optima and help achieve an optimal configuration of joint angles. The algorithm performs well in high-dimensional optimization problems, improves the movement accuracy and efficiency of the robotic arm, and reduces energy consumption. Experimental results show that the path planning and joint angle optimisation strategy combining A* and simulated annealing algorithms can effectively cope with the challenges in complex environments and improve the overall performance of the robotic arm. The research results of this paper provide theoretical support and practical guidance for the application of intelligent robots in complex environments, and have important academic and practical value. Future research can explore the combined application of other optimisation algorithms to further enhance the adaptability of robotic arms in a wider range of scenarios, so as to promote the sustainable development and innovation of robotics.