Safety Prioritization by Iterative Feedback Linearization Control for Collaborative Robots

Abstract

In today’s expanding landscape of intelligent autonomous robotic systems, their applications extend far beyond industrial settings, encompassing diverse operational domains and unconfined use cases, including close collaboration with humans. This proliferation of robots in human-centric environments necessitates advanced safety assurance methods, critical for achieving both physical and psychological safety, thereby fostering societal acceptance at the scale envisaged by the industry. Safety criteria vary across contexts, and diverse regulatory and standard organizations define safety differently. This paper introduces a versatile safe iterative feedback linearization control method capable of simultaneously addressing multiple safety scenarios and dynamically adjusting their priorities based on real-time conditions. Combining classic feedback linearization synthesis with nonlinear model predictive control and introducing a safety indicator, this approach offers adaptability in safety constraint prioritization. Experimental validation on a collaborative robot arm illustrates the method’s efficiency and flexibility in managing safety constraints, underscoring its promise for safe human-robot interactions.