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With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle–robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.

This paper proposes the design of a weighted-rotor energy harvester (WREH) in which the oscillation is caused by the periodic change of the tangential component of gravity, to harvest kinetic energy from a rotating wheel. When a WREH is designed with a suitable characteristic length, the rotor's natural frequency changes according to the wheel rotation speed and the rotor oscillates at a wide angle and high angular velocity to generate a large amount of power. The magnetic disk is designed according to an optimized circular Halbach array. The optimized circular Halbach array magnetic disk provides the largest induced EMF for different sector-angle ratios for the same magnetic disk volume. This study examined the output voltage and power by considering the constant and accelerating plate-rotation speeds, respectively. This paper discusses the effects of the angular acceleration speed of a rotating wheel corresponding to the dynamic behaviors of a weighted rotor. The average output power is 399 to 535 microwatts at plate-rotation speeds from 300 to 500 rpm, enabling the WREH to be a suitable power source for a tire-pressure monitoring system.

The maturity of automatic driving and parking technologies is gradually driving electric vehicle charging toward automation. The primary condition of automatic charging that has a high significance is the identification of electric vehicle charging ports. This research proposes an automatic system for the identification and positioning of charging ports of electric vehicles. The system is mainly divided into rough and precise positioning. The former is based on the Hough circle and the Hough line, and locates the position information of the charging port. The latter uses the Canny operator to obtain the contour information of the original and gradient images respectively. All the contours of the two images are fitted into ellipses by the quadratic curve standardization (QCS) method, and irrelevant ellipses are screened out. Finally, the perspective-n-point (PNP) algorithm is used to locate the pose information of the charging port. The aubo-i10 6-DOF articulated robot is used to test the recognition and insertion accuracies in different environments. The results show that the average recognition rate of rough positioning is 97.9%, the average displacement error of precise positioning in X, Y and Z directions are 0.60, 0.83 and 1.23mm, respectively, and the average angle errors in RX, RY and RZ directions are 1.19, 0.97 and 0.50 degrees, respectively. The average success rate is 94.8%. These results demonstrate that the proposed system meets the basic plug-in requirements of electric vehicle charging ports. INDEX TERMSAutomatic charging, electric vehicle charging port, pose estimation, monocular vision, non-cooperative characteristics P. Quan et al.: Research on Fast Identification and Location of Contour Features of Electric Vehicle Charging Port in Complex Scenes PENGKUN QUAN received the B.S. degree in mechanical engineering from the Tianjin Agricultural University, Tianjin, China, in 2017, and the M.D. degree in Agricultural Engineering from the Northwest A&F University, Xianyang, China, in 2019. He is currently pursuing the Ph.D. degree in mechanical engineering with the Harbin Institute of Technology, Harbin, China.He has published over 4 articles and won the first prize of China graduate electronic design competition in 2018. His current research interests include computer vision and cable-driven auto-charging robot for electric vehicles.

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