Adaptive Admittance Control for Safety-Critical Physical Human Robot Collaboration

Abstract

Physical human-robot collaboration requires strict safety guarantees, due to the fact that robots and humans work in a shared workspace. This paper presents a novel control framework to handle safety-critical position-based constraints for human-robot physical interaction. The proposed methodology is based on admittance control, exponential control barrier functions (ECBFs), and quadratic program (QP) to achieve compliance during the force interaction between human and robot, while simultaneously guaranteeing safety constraints. In particular, the formulation of admittance control is formulated as a second-order nonlinear control system, and the interaction forces between humans and robots are regarded as the control input. A virtual force feedback for admittance control is provided in real-time by using the ECBFs-QP framework as a compensator of the external human forces. A safe trajectory is therefore derived from the proposed adaptive admittance control scheme for a low-level controller to track. The main innovation of the proposed approach is the ability to enable the robot to naturally comply with human forces without violating any safety constraints, even when external human forces incidentally force the robot to do so. The effectiveness of our approach is demonstrated in simulation studies on a two-link planar robot manipulator.