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.

With the increasing popularity of electric vehicles, low level of automation for charging process has become one of the main factors restricting the development of electric vehicles. Recently, auto-charging robots which have the ability to transform manual process plugging charging plugs into charging ports to automatic plugging-unplugging operation have arisen. This paper presents a 4-DOF cable-driven autocharging robot (CDACR) consisting of a 3-DOF cable-driven serial manipulator (CDSM) and a moving platform. In this design, the 3-DOF CDSM is actuated by six cables being routed through five disks fixed to the CDSM's rigid links. The end-effector of CDACR is a flexible plug that has the ability to withstand small elastic deformation. The control algorithm and the plugging-unplugging strategy were designed to respond to various parking situations with or without yaw error. This paper takes the lead in introducing the cable-driven robot into the field of automatic charging. Besides, through simulated charging experiments, the feasibility and effectiveness of using CDACR to realize auto-charging for electric vehicles has been demonstrated.INDEX TERMS Cable-driven robot, auto-charging robot, electric vehicles.

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.

The structure of the cable-driven serial manipulator (CDSM) is more complex than that of the cable-driven parallel manipulator (CDPM), resulting in higher model complexity and stronger structural and parametric uncertainties. These drawbacks challenge the stable trajectory-tracking control of a CDSM. To circumvent these drawbacks, this paper proposes a robust adaptive controller for an n-degree-of-freedom (DOF) CDSM actuated by m cables. First, two high-level controllers are designed to track the joint trajectory under two scenarios, namely known and unknown upper bounds of uncertainties. The controllers include an adaptive feedforward term based on inverse dynamics and a robust control term compensating for the uncertainties. Second, the independence of control gains from the upper bound of uncertainties and the inclusion of the joint viscous friction coefficient into the dynamic parameter vector are realised. Then, a low-level controller is designed for the task of tracking the cable tension trajectory. The system stability is analysed using the Lyapunov method. Finally, the validity and effectiveness of the proposed controllers are verified by experimenting with a three-DOF six-cable CDSM. In addition, a comparative experiment with the classical proportional–integral–derivative (PID) controller is carried out.

With the gradual maturity of driverless and automatic parking technologies, electric vehicle charging has been gradually developing in the direction of automation. However, the pose calculation of the charging port (CP) is an important part of realizing automatic charging, and it represents a problem that needs to be solved urgently. To address this problem, this paper proposes a set of efficient and accurate methods for determining the pose of an electric vehicle CP, which mainly includes the search and aiming phases. In the search phase, the feature circle algorithm is used to fit the ellipse information to obtain the pixel coordinates of the feature point. In the aiming phase, contour matching and logarithmic evaluation indicators are used in the cluster template matching algorithm (CTMA) proposed in this paper to obtain the matching position. Based on the image deformation rate and zoom rates, a matching template is established to realize the fast and accurate matching of textureless circular features and complex light fields. The EPnP algorithm is employed to obtain the pose information, and an AUBO-i5 robot is used to complete the charging gun insertion. The results show that the average CP positioning errors (x, y, z, Rx, Ry, and Rz) of the proposed algorithm are 0.65 mm, 0.84 mm, 1.24 mm, 1.11 degrees, 0.95 degrees, and 0.55 degrees. Further, the efficiency of the positioning method is improved by 510.4% and the comprehensive plug-in success rate is 95%. Therefore, the proposed CTMA in this paper can efficiently and accurately identify the CP while meeting the actual plug-in requirements.