Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images
Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images
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DOI：
10.1109/icra.2019.8794226
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Yuval Litvak;A. Biess;Aharon Bar-Hillel
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作者：
Yuval Litvak;A. Biess;Aharon Bar-Hillel
当今大多数工业机器人装配任务需要固定的初始条件才能成功装配。这些限制导致了高生产成本和对新任务的低适应性。在这项工作中，我们的目标是通过使用3D CAD模型的所有部件进行组装，实现灵活和适应性强的机器人装配。我们专注于一个通用的装配任务-西门子创新挑战赛-在这个任务中，机器人需要将一个高精度的齿轮状机构装配到操作系统中。为了获得这项任务和工业设置所需的毫米级精度，我们使用了安装在机器人末端执行器附近的深度相机。我们提出了一种基于深度卷积神经网络的高精度两阶段姿态估计过程，其中包括检测、姿态估计、细化以及处理部件的接近和完全对称性。网络在模拟深度图像上进行训练，以确保成功传输到真实的机器人。我们得到的平均姿态估计误差为2.16毫米和0.64度，导致91%的成功率为随机分布的零件的机器人装配。据我们所知，这是西门子创新挑战赛首次得到全面解决，所有部件的组装成功率都很高。
Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new tasks. In this work we aim towards flexible and adaptable robotic assembly by using 3D CAD models for all parts to be assembled. We focus on a generic assembly task - the Siemens Innovation Challenge - in which a robot needs to assemble a gear-like mechanism with high precision into an operating system. To obtain the millimeter-accuracy required for this task and industrial settings alike, we use a depth camera mounted near the robot’s end-effector. We present a high-accuracy two-stage pose estimation procedure based on deep convolutional neural networks, which includes detection, pose estimation, refinement, and handling of near- and full symmetries of parts. The networks are trained on simulated depth images with means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.16 millimeters and 0.64 degree leading to 91% success rate for robotic assembly of randomly distributed parts. To the best of our knowledge, this is the first time that the Siemens Innovation Challenge is fully addressed, with all the parts assembled with high success rates.