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 automation of the charging task research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, 5 mm, and 10 degrees. 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 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 movements of the charger. Eventually, an algorithm was developed based on the human's best strategies to be applied to a robotic arm.

翻译：电动汽车日益增长的需求推动了自动充电方法的发展。目前，电动汽车的充电过程完全依赖手动操作，需要耗费体力完成，这对残障人士并不友好。现有研究多集中于检测插座位置与姿态以实现充电自动化，虽已达到较高精度（5毫米及10度），但仍不足以完整完成充电流程。本研究聚焦于设计一种基于人类触觉的鲁棒性机器人插拔新方法，以克服插座姿态误差。我们邀请参与者执行充电任务，通过测量充电器施加的力及运动轨迹识别其认知能力，最终基于人类最优策略开发出可应用于机械臂的算法。