Underactuated Gripper with Forearm Roll Estimation for Human Limbs Manipulation in Rescue Robotics

The emergence of new robotic technologies such as compliant control and soft robotics, has contributed to safe physical Human-Robot Interaction (pHRI) mainly for assistive applications. However, a robot capable of directly manipulating the human body, which is key for the implementation of autonomous rescue robots, has not been developed so far. In this paper, the development of a gripper and methods for the robotic manipulation of a laying victim’s forearm, initiated by the robot is addressed, and validated based on experimental results. An underactuated gripper with added proprioceptive sensors has been designed, with environment sensing and tactile recognition capabilities. This method provides a stable grasping of a human forearm that lays on a surface and is capable of estimating the roll angle of the grasped arm for precise location and safe manipulation. The roll-angle estimation method is based on Machine Learning and has been trained with experimental data obtained from experiments with human volunteers. The resulting method provides robust and precise grasping, tolerant to location inaccuracy with inexpensive sensors. This is one of the very first works on the robotic human-body manipulation.