When a robot performs a grinding operation, the steady-state force/position tracking accuracy of its end-effector is critical to achieving high grinding quality. To solve this problem, a fuzzy adaptive impedance method is incorporated into the robot’s compliant control framework. Firstly, the robot dynamics model is established based on the Newton–Euler method. To describe the robot dynamics more comprehensively, a linear friction compensation model is also introduced. Secondly, a dynamic feedforward trajectory-tracking controller is proposed based on the dynamic model, and its stability is verified using a Lyapunov function. The impedance parameters are adjusted in real time according to the feedback contact force and its rate of change, thereby enabling dynamic equilibrium between the end contact force and end position. This allows the robot end-effector to exhibit compliance during external environmental interactions. Finally, a control platform of a force/position compliance controller was constructed, and two grinding conditions of plane and arc were designed to validate the effectiveness of force/position compliance control based on impedance control. Compared with the fixed impedance approach, the proposed method reduces overshoot by 11.6% (plane) and 12.45% (arc), improves surface roughness from Ra 0.042 μm to Ra 0.021 μm, and achieves faster force tracking with fewer oscillations.
Fan Yang, Ming Hu, Jin-Fei Bian et al.· Machines· 0 citations
Abstract. Superpoint matching is a critical step in coarse-to-fine point cloud registration, and its performance directly affects the accuracy of subsequent point matching and pose estimation. However, most existing methods establish correspondences mainly relying on feature similarity, without explicit modeling of spatial structure, which easily leads to unstable matching in complex scenarios such as noise, occlusion, and low overlap. To address these issues, this paper proposes a coarse-to-fine point cloud registration method guided by prior correspondences. First, prior superpoint correspondences are constructed using rigid transformations estimated by existing SOTA methods, and are serially encoded via a prior encoding module to provide explicit constraints for feature learning. Furthermore, multiple geometric information including pairwise distances, angles, and normals is introduced and uniformly encoded to enhance spatial struc-ture representation. On this basis, a prior-guided sparse mixture-of-experts attention mechanism is designed to differentially model features in overlapping and non-overlapping regions, thereby improving feature discriminability and structural consistency. Using the learned features, the model gradually establishes correspondences through superpoint matching and point matching, and estimates the final rigid transformation with RANSAC. Experiments on the 3DMatch dataset show that when sampling 1000 point correspondences, the proposed method achieves an inlier ratio of 80.7% and a registration recall of 92.9%, which are 5.5% and 1.1% higher than the baseline method respectively, verifying the effectiveness of the proposed method in terms of accuracy and robustness.
Meng Sun, Juntao Yang, Yutao Zhang et al.· The International Archives o...· 0 citations
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