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This study aims to solve the problem of the existing metal foreign object (MFO) detecting systems, which are not sensitive to the small size MFO in wireless charging region of electric vehicle (EV) because of the extremely complex signal noise in the process of wireless charging of EV.

A method for MFO detection based on the principle that MFOs can cause mistuned resonance of detection coil resonant circuit is proposed. The general scheme of detecting system is proposed. The design method for detection coils which is effective to small MFOs detection in large-area region of wireless charging of EV is presented. The design of time-sharing driving circuit and amplifying circuit of high frequency exciting signal for detection coils is introduced. The design scheme of signal processing circuit (including filter and rectifier) of detection coil terminal voltage is also proposed.

The influence of exciting frequency of detection coils on detecting sensitivity and the anti-noise feature of system are analyzed according to the experiment results.

The experiment of MFO detection indicates that the proposed method can effectively detect the coin-sized small MFO in the process of wireless charging of EV.

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.