基于强化学习的轴套自动装配机器人

IF 2.9 3区 工程技术 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Advanced Manufacturing Technology Pub Date : 2024-03-21 DOI:10.1007/s00170-024-13467-1
Xumiao Ma, De Xu
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引用次数: 0

摘要

轴套装配是工业制造中的一项常见任务。轴套装配的配合方法通常是过盈配合,需要很大的接触力。传统的装配方法虽然注重安全性,但往往难以实现高效率。强化学习可以通过与环境的交互有效地选择适当的装配动作,因此非常适合轴套装配任务。首先,制定了轴套装配的综合工作流程,包括系统初始化、插入、推动和完成。我们的研究主要集中在插入过程。其次,核心控制算法采用了基于行为批判架构的深度强化学习方法。奖励函数包括安全奖励、步长奖励和步长奖励。安全奖励确保了装配安全,而步长奖励和步数奖励则从不同角度提高了装配效率。最后,进行了轴套装配的实际实验,包括烧蚀实验、奖励函数的参数调整实验以及与传统方法的对比实验。烧蚀实验和参数调整实验的结果表明,将安全奖励、步长奖励和阶跃奖励相结合后,装配效果最佳,验证了所提出的奖励函数的有效性。对比实验结果表明,与传统方法相比,我们的方法不仅增强了安全性,还显著提高了装配效率,说明这种方法是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Automated robotic assembly of shaft sleeve based on reinforcement learning

Shaft sleeve assembly is a common task in industrial manufacturing. The fitting approach for shaft sleeve assembly is usually interference fit, which requires significant contact forces. Conventional assembly methods, though focused on safety, often struggle to achieve high efficiency. Reinforcement learning can effectively select appropriate assembly actions through interaction with the environment, making it well-suited for shaft sleeve assembly tasks. Firstly, a comprehensive workflow for shaft sleeve assembly is formulated, including system initialization, insertion, push, and completion. Our research focuses mainly on the insertion process. Secondly, the core control algorithm adopts a deep reinforcement learning method based on the Actor-Critic architecture. The reward function includes safety reward, step length reward, and step reward. Safety reward ensures assembly security, while step length and step reward enhance assembly efficiency from different perspectives. Finally, real-world experiments on shaft sleeve assembly are conducted, including ablation experiments, parameter tuning experiments on reward function, and comparative experiments with conventional methods. The results of the ablation experiments and parameter tuning experiments indicate that after combining safety reward, step length reward, and step reward, the assembly effect achieves the best, verifying the effectiveness of the proposed reward function. Comparative experimental results demonstrate that our approach not only enhances safety compared to conventional methods but also significantly improves assembly efficiency, indicating the feasibility of this method.

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来源期刊
CiteScore
5.70
自引率
17.60%
发文量
2008
审稿时长
62 days
期刊介绍: The International Journal of Advanced Manufacturing Technology bridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration.
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