Department of Electrical and Computer Engineering, University of California Davis, Davis, CA 95616, USA.
Greenfield Labs, Ford Motor Company, Palo Alto, CA 94304, USA.
Sensors (Basel). 2023 Jul 2;23(13):6107. doi: 10.3390/s23136107.
Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors.
姿态估计对于自动化装配任务至关重要，但要实现足够的装配自动化精度仍然具有挑战性且针对特定部件。本文提出了一种新颖的、简化的姿态估计方法，以促进装配任务的自动化。我们提出的方法在有限数量的标注图像上使用深度学习来识别感兴趣部件上的一组关键点。为了弥补网络的不足并提高准确性，我们结合了我们对装配部件设计的详细了解，引入了贝叶斯更新阶段。该贝叶斯更新步骤优化了网络输出，显著提高了姿态估计的准确性。为此，我们利用了具有更高质量的网络生成的关键点位置子集作为测量值，而对于其余的关键点，网络输出仅作为先验。几何数据有助于构建似然函数，进而生成优化后的关键点像素位置后验分布。然后，我们采用最大后验（MAP）估计来获取关键点的位置，从而获得最终的姿态，以实现名义装配轨迹的更新。我们在福特野马仪表板的 14 点快速卡扣式仪表饰条装配上评估了我们的方法，结果表明效果良好。我们的方法不需要针对新应用进行定制，也不需要依赖大量的机器学习专业知识或训练数据。这使得我们的方法成为生产车间的一种可扩展和适应性强的解决方案。
