基于力的带键销孔装配偏移估计：采用局部高斯过程回归
Force-Based Offset Estimation for Keyed Peg-in-Hole Assembly Using Local Gaussian Process Regression
- Institute of Mechanical Engineering, Cologne University of Applied Sciences（科隆应用技术大学机械工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对键-键槽装配的几何约束与位姿偏差问题，提出嵌入感知-验证-插入流程的力偏移估计方法，通过局部KNN-高斯过程混合回归器等提升精度，使协作机械臂销插入成功率从67%升至87%。
AI中文摘要：
键-键槽装配任务施加了严格的几何约束，在不确定环境中对抓取位姿偏差高度敏感。本研究提出一种用于带键销孔装配的基于力的偏移估计方法，嵌入在感知-验证-插入流程中。利用腕力/力矩测量值，通过局部KNN-高斯过程混合回归器直接估计残余未对准。该框架区分两种接触状态：硬碰撞和引导倒角插入，并将推理分配给每种状态的专用模型，状态分类通过接触窗口持续时间阈值实现。KNN结合抓取后单目视觉验证结果的确定性搜索，提升了回归模型的精度。该方法在基于关键点检测的抓取放置应用中，实现了倒角销插入的精确径向偏移估计。使用协作机械臂的集成力/力矩传感器开展的实验显示，应用该流程后，插入成功率从67%提升至87%。
英文摘要：
Key-keyway assembly tasks impose strict geometric constraints and are highly sensitive to grasp pose deviations in uncertain environments. This work presents a force-based offset estimation method for keyed peg-in-hole assembly, embedded within a perception-validation-insertion pipeline. Residual misalignment is estimated directly from wrist force/torque measurements using a local KNN-Gaussian Process hybrid regressor. The framework distinguishes between two contact regimes, hard collision and guided chamfer insertion, and routes inference to a dedicated model for each. Regime classification is achieved via a contact-window duration threshold. KNN combined with a deterministic search using the results of a post-grasp monocular visual validation contributes to an increased accuracy of the regressor model. This approach achieves accurate radial offset estimation in chamfered peg insertion, during a keypoint detection-based pick and place application. Experiments using the integrated force/torque sensor of a collaborative robot arm showed an increase in insertion success rate from 67% to 87% after the pipeline was applied.
