使用多视图图像和基于合成数据训练的深度网络快速将钉插入不确定的孔中
Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data
摘要：
本文使用机器人将钉子组装到具有不同颜色和纹理的表面上的孔中。特别针对具有初始位置不确定性的轴孔装配问题。两个手持摄像机和一个力-扭矩传感器被用来考虑位置的不确定性。执行包括基于学习的视觉伺服、螺旋搜索和阻抗控制的程序序列，以利用来自上述传感器的反馈来执行孔内钉任务。贡献主要是在序列的基于学习的视觉伺服中做出的，其中使用域随机化的概念生成的各种合成数据集来训练深度神经网络，以预测孔的位置。在实验和分析部分，网络进行了分析和比较，并实现了一个真实世界的机器人系统，使用所提出的方法插入钉孔。结果表明，实现的钉孔装配系统可以执行成功的钉孔插入不同颜色和纹理的表面。它通常可以加快整个钉孔过程。
英文摘要：
This paper uses robots to assemble pegs into holes on surfaces with different colors and textures. It especially targets at the problem of peg-in-hole assembly with initial position uncertainty. Two in-hand cameras and a force-torque sensor are used to account for the position uncertainty. A program sequence comprising learning-based visual servoing, spiral search, and impedance control is implemented to perform the peg-in-hole task with feedback from the above sensors. Contributions are mainly made in the learning-based visual servoing of the sequence, where a deep neural network is trained with various sets of synthetic data generated using the concept of domain randomization to predict where a hole is. In the experiments and analysis section, the network is analyzed and compared, and a real-world robotic system to insert pegs to holes using the proposed method is implemented. The results show that the implemented peg-in-hole assembly system can perform successful peg-in-hole insertions on surfaces with various colors and textures. It can generally speed up the entire peg-in-hole process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
