The adjustment times of the attitude alignment are fluctuated due to the fluctuation of the contact force signal caused by the disturbing moments in the compliant peg-in-hole assembly. However, these fluctuations are difficult to accurately measure or definition as a result of many uncertain factors in the working environment. It is worth noting that gravitational disturbing moments and inertia moments significantly impact these fluctuations, in which the changes of the peg concerning the mass and the length have a crucial influence on them. In this paper, a visual grasping strategy based on deep reinforcement learning is proposed for peg-in-hole assembly. Firstly, the disturbing moments of assembly are analyzed to investigate the factors for the fluctuation of assembly time. Then, this research designs a visual grasping strategy, which establishes a mapping relationship between the grasping position and the assembly time to improve the assembly efficiency. Finally, a robotic system for the assembly was built in V-REP to verify the effectiveness of the proposed method, and the robot can complete the training independently without human intervention and manual labeling in the grasping training process. The simulated results show that this method can improve assembly efficiency by 13.83%. And, when the mass and the length of the peg change, the proposed method is still effective for the improvement of assembly efficiency.

中文翻译：

---

基于深度强化学习的提高装配效率的视觉抓取策略

由于柔性孔中钉组件中的干扰力矩引起接触力信号的波动，导致姿态对准的调整时间波动。然而，由于工作环境中的许多不确定因素，这些波动很难准确测量或定义。值得注意的是，重力扰动矩和惯性矩对这些波动有显着影响，其中桩的质量和长度的变化对它们有至关重要的影响。在本文中，提出了一种基于深度强化学习的视觉抓取策略，用于孔内装配。首先，通过对装配扰动矩的分析，考察装配时间波动的影响因素。然后，本研究设计了一种视觉抓取策略，建立抓取位置与装配时间的映射关系，提高装配效率。最后，在V-REP中构建了用于装配的机器人系统以验证所提出方法的有效性，机器人在抓取训练过程中无需人工干预和人工标注即可独立完成训练。仿真结果表明，该方法可将装配效率提高13.83%。并且，当桩的质量和长度发生变化时，所提出的方法对于提高装配效率仍然有效。并且机器人在抓取训练过程中可以独立完成训练，无需人工干预和人工标注。仿真结果表明，该方法可将装配效率提高13.83%。并且，当桩的质量和长度发生变化时，所提出的方法对于提高装配效率仍然有效。并且机器人在抓取训练过程中可以独立完成训练，无需人工干预和人工标注。仿真结果表明，该方法可将装配效率提高13.83%。并且，当桩的质量和长度发生变化时，所提出的方法对于提高装配效率仍然有效。