In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

Traditional sensors for monitoring oil wear debris do not integrate the advantages of high sensitivity and large flow, limiting the performance of the sensors. Thus, in this work, we design a sensitive and large-flow sensor based on the dual-excitation and multi-induction structure. The finite element analysis (FEA) shown that the impact of width of the L-shaped magnetic poles (L-MPs) on magnetic field is not serious and the influence of crosstalk among multiple medium-sized induction elements (MIEs) can be ignored. Therefore, for our sensor structure, the short distance between two excitation elements (EEs) can generate a strong symmetrically gradient magnetic field, which ensures the high sensitivity of the sensor. Meanwhile, thanks to the wide L-MPs, multiple MIEs can be arranged side by side, effectively increasing the flow of the sensor. Finally, a sensor containing four MIEs with an equivalent inner diameter of 20mm is made. The experiment shown that our sensor had no obvious crosstalk phenomenon during the work and successfully detected the iron debris of $25~\mu \text{m}$ , showing a better performance than existing sensors.