摘 要
随着工业4.0的深入推进，机器人与机电一体化设备的协同作业已成为智能制造领域的关键研究方向，其在提升生产效率、优化资源配置和实现柔性制造方面具有重要意义本研究旨在探索机器人与机电一体化设备之间的高效协同机制，通过构建统一的控制架构和信息交互模型，解决传统系统中信息孤岛和协作效率低下的问题研究采用多智能体系统理论结合深度强化学习算法，设计了一种自适应协同控制策略，能够根据任务需求动态调整机器人与机电一体化设备的工作模式实验结果表明，该策略显著提高了系统的响应速度和任务完成精度，相较于传统方法，整体生产效率提升了约25%此外，本研究提出了一种基于数字孪生技术的任务仿真与优化框架，可提前预测并优化协同作业中的潜在冲突，进一步增强了系统的稳定性和可靠性本研究的主要创新点在于将人工智能技术与传统制造系统深度融合，突破了机器人与机电一体化设备协同作业的技术瓶颈，为智能制造提供了新的理论支持和技术路径研究成果对推动智能工厂建设及提升制造业竞争力具有重要参考价值关键词：机器人协同；机电一体化；深度强化学习；数字孪生；智能制造
Abstract
With the deepening advancement of Industry 4.0, the collaborative operation between robots and mechatronic equipment has become a critical research direction in the field of intelligent manufacturing, playing a significant role in enhancing production efficiency, optimizing resource allocation, and achieving flexible manufacturing. This study aims to explore an efficient collaborative mechanism between robots and mechatronic equipment by constructing a unified control architecture and information interaction model, addressing the issues of information silos and low collaboration efficiency in traditional systems. By integrating multi-agent system theory with deep reinforcement learning algorithms, an adaptive collaborative control strategy is designed, which can dynamically adjust the working modes of robots and mechatronic equipment according to task requirements. Experimental results demonstrate that this strategy significantly improves the system's response speed and task completion accuracy, resulting in an approximate 25% increase in overall production efficiency compared to conventional methods. Furthermore, this study proposes a task simulation and optimization fr amework based on digital twin technology, which can predict and optimize potential conflicts in collaborative operations in advance, thereby further enhancing the stability and reliability of the system. The primary innovation of this research lies in the deep integration of artificial intelligence technologies with traditional manufacturing systems, breaking through the technical bottlenecks in the collaborative operation between robots and mechatronic equipment, and providing new theoretical support and technical pathways for intelligent manufacturing. The research findings hold important reference value for promoting the construction of smart factories and enhancing the competitiveness of the manufacturing industry..
Key Words：Robot Collaboration；Mechatronics；Deep Reinforcement Learning；Digital Twin；Intelligent Manufacturing
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状分析 1
1.3 本文研究方法与技术路线 2
第2章 协同作业的理论基础与关键技术 3
2.1 协同作业的基本概念与定义 3
2.2 机器人与机电一体化设备的系统架构 3
2.3 关键技术分析与应用现状 4
2.4 协同作业中的信息交互机制 4
第3章 协同作业中的任务规划与优化 6
3.1 任务分配与路径规划方法 6
3.2 多机器人系统的协同控制策略 6
3.3 机电一体化设备的任务协调机制 7
3.4 基于仿真的任务规划优化案例 7
第4章 实验验证与性能评估 9
4.1 实验平台设计与搭建 9
4.2 协同作业性能测试方法 9
4.3 数据采集与分析技术 10
4.4 实验结果与改进方向探讨 10
结 论 11
参考文献 13
致 谢 14
