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<p>With the rapid development of intelligent manufacturing, the role of robots in industrial production has become increasingly prominent. Existing robot path planning methods have many shortcomings in complex environments, such as easy to fall into local optimum, inefficient search and insufficient path accuracy. To this end, the study proposes a machine tool assisted robot control method based on an improved fast extended random tree algorithm and proportional-integral-derivative control. The proportional-integral-differential control control strategy is combined with feedforward control by introducing pruning strategy and target deviation strategy. The experimental results show that the proposed method outperforms the traditional method in terms of performance metrics such as path length, search time, trajectory deviation and success rate. Specifically, the method achieves a success rate of 99.87%, the search time is only 0.354&#xa0;s, and the absolute value of trajectory deviation is only 0.020&#xa0;m. Compared with other models, the planning success rate is improved by 17.31%. This research achievement provides a new solution for the efficient and precise operation of machine tool-assisted robots in complex industrial environments, demonstrating strong innovation and practical potential at the theoretical level.</p>

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Machine Tool Assisted Robot Control Method Based on RRT Algorithm

  • 期刊论文

  • 发表日期：

    2025年12月2日

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  • Ruiling Hu

摘要

With the rapid development of intelligent manufacturing, the role of robots in industrial production has become increasingly prominent. Existing robot path planning methods have many shortcomings in complex environments, such as easy to fall into local optimum, inefficient search and insufficient path accuracy. To this end, the study proposes a machine tool assisted robot control method based on an improved fast extended random tree algorithm and proportional-integral-derivative control. The proportional-integral-differential control control strategy is combined with feedforward control by introducing pruning strategy and target deviation strategy. The experimental results show that the proposed method outperforms the traditional method in terms of performance metrics such as path length, search time, trajectory deviation and success rate. Specifically, the method achieves a success rate of 99.87%, the search time is only 0.354 s, and the absolute value of trajectory deviation is only 0.020 m. Compared with other models, the planning success rate is improved by 17.31%. This research achievement provides a new solution for the efficient and precise operation of machine tool-assisted robots in complex industrial environments, demonstrating strong innovation and practical potential at the theoretical level.