Proprioceptive Estimation of Forces Using Underactuated Fingers for Robot-Initiated pHRI

Abstract

In physical Human–Robot Interaction (pHRI), forces exerted by humans need to be estimated to accommodate robot commands to human constraints, preferences, and needs. This paper presents a method for the estimation of the interaction forces between a human and a robot using a gripper with proprioceptive sensing. Specifically, we measure forces exerted by a human limb grabbed by an underactuated gripper in a frontal plane using only the gripper’s own sensors. This is achieved via a regression method, trained with experimental data from the values of the phalanx angles and actuator signals. The proposed method is intended for adaptive shared control in limb manipulation. Although adding force sensors provides better performance, the results obtained are accurate enough for this application. This approach requires no additional hardware: it relies uniquely on the gripper motor feedback—current, position and torque—and joint angles. Also, it is computationally cheap, so processing times are low enough to allow continuous human-adapted pHRI for shared control.

A Comprehensive Pattern Recognition Neural Network for Collision Classification Using Force Sensor Signals

“…During this collaboration, potential injuries may happen to the human, and other risks can also be encountered. Thus, a method for estimating and then detecting the collisions that probably happen or classifying them should be implemented [1,2]. Data-based approaches are widely used for this purpose and are our concentration in the current work.…”