The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, $\pm 5 \: mm $ and $\pm 10^o$. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics, to overcome the error in the position and orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movement of the charger. Three controllers were designed based on impedance control to mimic the human patterns of charging an electric car. The recorded data from humans were used to calibrate the parameters of the impedance controllers: inertia $M_d$, damping $D_d$, and stiffness $K_d$. A robotic validation was performed, where the designed controllers were applied to the robot UR10. Using the proposed controllers and the human kinesthetic data, it was possible to successfully automate the operation of charging an electric car.

翻译：电动汽车日益增长的需求推动了自动化充电方法的发展。目前，电动汽车的充电过程完全依赖人工操作，且需要耗费体力完成此任务，这对残障人士并不友好。现有研究通常聚焦于检测插座的位置与方向，已实现较高精度（±5毫米和±10°）。然而，该精度仍不足以完成充电流程。本文设计了一种基于人类触觉的鲁棒机器人插拔式充电新型方法，以克服插座位置与方向的误差。我们邀请参与者执行充电任务，通过测量施加力及充电器运动轨迹识别其认知能力。基于阻抗控制原理，我们设计了三种控制器以模拟人类为电动汽车充电的行为模式。人类操作记录数据用于校准阻抗控制器的参数：惯性M_d、阻尼D_d和刚度K_d。通过机器人验证实验，将所设计的控制器应用于UR10机器人。采用所提出的控制器与人类动觉数据，成功实现了电动汽车充电操作的自动化。