Safety-Critical Control for Aerial Physical Interaction in Uncertain Environment

TL;DR

This paper introduces a disturbance-observer-based safety-critical control method for aerial manipulators that ensures safe physical interaction with static and dynamic structures, maintaining motor thrust limits even with disturbances.

Contribution

The paper presents a novel safety filter that dynamically adjusts the vehicle's trajectory considering disturbances, dynamics, and motor limits, with rigorous safety guarantees.

Findings

The method ensures forward invariance of the safety set under disturbances.

Experimental results show superior performance over existing control strategies.

The approach effectively handles rapid dynamic changes in complex tasks.

Abstract

Aerial manipulation for safe physical interaction with their environments is gaining significant momentum in robotics research. In this paper, we present a disturbance-observer-based safety-critical control for a fully actuated aerial manipulator interacting with both static and dynamic structures. Our approach centers on a safety filter that dynamically adjusts the desired trajectory of the vehicle's pose, accounting for the aerial manipulator's dynamics, the disturbance observer's structure, and motor thrust limits. We provide rigorous proof that the proposed safety filter ensures the forward invariance of the safety set - representing motor thrust limits - even in the presence of disturbance estimation errors. To demonstrate the superiority of our method over existing control strategies for aerial physical interaction, we perform comparative experiments involving complex tasks, such as pushing against a static structure and pulling a plug firmly attached to an electric socket. Furthermore, to highlight its repeatability in scenarios with sudden dynamic changes, we perform repeated tests of pushing a movable cart and extracting a plug from a socket. These experiments confirm that our method not only outperforms existing methods but also excels in handling tasks with rapid dynamic variations.