Compensation of electrical current drift in human-robot collision.

Abstract

Human-robot collaborative systems are being increasingly adopted in manufacturing environments due to their application flexibility, adaptability, and cost-effectiveness. The majority of robotic systems use electrical current sensors to measure joint torque in industrial robot arms and limit the robot's impact in the event of an unanticipated acceleration/deceleration, such as in the event of a collision with a human operator. However, these electrical current sensors are known to experience sensor drift, which results in measurement inaccuracy that can result in improper joint-torque or end-effector force readings. This paper provides a method to compensate for electrical current drift using a neural network-based controller to control robot velocity. To evaluate the compensation method, an experimental setup was developed where a robot joint collided with the biofidelic test device that mimics the deformation response of the human forearm while simultaneously measuring deformation and contact force using an embedded soft force sensor. The proposed method was shown to compensate for electrical current drift and therefore reduce resulting contact forces between the robot and the biofidelic test device. Hence, this research provides a method to quantify the behavior of electrical current sensor drift on human-robot collision and presents a data-driven methodology for compensation.