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：电动汽车需求的增长催生了自动化充电方法的开发需求。目前，电动汽车充电过程完全依赖人工操作，需要耗费体力完成充电任务，这对残障人士并不友好。现有研究通常将充电自动化重点聚焦于插座位置与姿态的检测，已实现5毫米和10度的高精度定位。然而，这一精度仍不足以完成完整的充电流程。本研究致力于设计一种基于人类触觉的鲁棒性机器人插拔充电新方法，以克服插座姿态误差。我们邀请受试者执行充电任务，通过测量充电器运动过程中施加的力来识别其认知能力。最终基于人类最优策略开发出可应用于机械臂的创新算法。