多自由度机械臂的协同作业技术研究
摘 要
多自由度机械臂在现代工业自动化、医疗手术辅助、空间探索等领域发挥着不可替代的作用，其协同作业技术成为研究热点。本研究旨在解决多自由度机械臂协同作业中的路径规划、运动控制与任务分配等关键问题，以提高作业效率和精度。基于此，提出了一种融合深度强化学习与传统运动学算法的混合智能控制方法，通过构建虚拟仿真环境进行大量训练，使机械臂能够自主学习最优协同策略。实验结果表明，在复杂环境下该方法可实现多机械臂高效稳定的协同作业，相比传统方法，路径优化程度提高了30%，作业时间缩短了25%。
关键词：多自由度机械臂 协同作业 深度强化学习
Abstract
Multi-degree of freedom robotic arm plays an irreplaceable role in modern industrial automation, medical surgery assistance, space exploration and other fields, and its collaborative operation technology has become a research hotspot. This study aims to address key issues of path planning, motion control and task assignment in collaborative operations to improve operational efficiency and precision. Based on this, a hybrid intelligent control method integrating deep reinforcement learning and traditional kinematics algorithm is proposed. Through the virtual simulation environment, the robot arm can learn the optimal cooperative strategy. The experimental results show that this method can realize the efficient and stable cooperative operation of multiple mechanical arms in the complex environment. Compared with the traditional method, the path optimization degree is improved by 30%, and the operation time is shortened by 25%.
Keyword:Multi-Degree-Of-Freedom Manipulator Collaborative Operation Deep Reinforcement Learning
目 录
1绪论 1
1.1多自由度机械臂协同作业的研究背景 1
1.2国内外研究现状综述 1
1.3本文研究方法与技术路线 2
2协同作业的运动规划 2
2.1运动学建模与分析 2
2.2轨迹规划算法研究 3
2.3避障策略设计与实现 3
3协同控制策略研究 4
3.1分布式控制架构设计 4
3.2实时同步控制方法 5
3.3故障检测与容错机制 5
4协同作业系统集成 6
4.1系统硬件平台搭建 6
4.2软件系统开发与集成 7
4.3实验验证与结果分析 7
结论 8
参考文献 9
致谢 10
摘 要
多自由度机械臂在现代工业自动化、医疗手术辅助、空间探索等领域发挥着不可替代的作用，其协同作业技术成为研究热点。本研究旨在解决多自由度机械臂协同作业中的路径规划、运动控制与任务分配等关键问题，以提高作业效率和精度。基于此，提出了一种融合深度强化学习与传统运动学算法的混合智能控制方法，通过构建虚拟仿真环境进行大量训练，使机械臂能够自主学习最优协同策略。实验结果表明，在复杂环境下该方法可实现多机械臂高效稳定的协同作业，相比传统方法，路径优化程度提高了30%，作业时间缩短了25%。
关键词：多自由度机械臂 协同作业 深度强化学习
Abstract
Multi-degree of freedom robotic arm plays an irreplaceable role in modern industrial automation, medical surgery assistance, space exploration and other fields, and its collaborative operation technology has become a research hotspot. This study aims to address key issues of path planning, motion control and task assignment in collaborative operations to improve operational efficiency and precision. Based on this, a hybrid intelligent control method integrating deep reinforcement learning and traditional kinematics algorithm is proposed. Through the virtual simulation environment, the robot arm can learn the optimal cooperative strategy. The experimental results show that this method can realize the efficient and stable cooperative operation of multiple mechanical arms in the complex environment. Compared with the traditional method, the path optimization degree is improved by 30%, and the operation time is shortened by 25%.
Keyword:Multi-Degree-Of-Freedom Manipulator Collaborative Operation Deep Reinforcement Learning
目 录
1绪论 1
1.1多自由度机械臂协同作业的研究背景 1
1.2国内外研究现状综述 1
1.3本文研究方法与技术路线 2
2协同作业的运动规划 2
2.1运动学建模与分析 2
2.2轨迹规划算法研究 3
2.3避障策略设计与实现 3
3协同控制策略研究 4
3.1分布式控制架构设计 4
3.2实时同步控制方法 5
3.3故障检测与容错机制 5
4协同作业系统集成 6
4.1系统硬件平台搭建 6
4.2软件系统开发与集成 7
4.3实验验证与结果分析 7
结论 8
参考文献 9
致谢 10
