Safety-Critical Control for Aerial Physical Interaction in Uncertain Environment

TL;DR

This work tackles safe aerial physical interaction under external disturbances and actuator limits by integrating a disturbance-observer (DOB) based controller with a safety filter. The safety layer employs barrier functions and a quadratic program to enforce motor-thrust constraints while the DOB-based control drives the system toward a safety-filtered target pose, with theoretical guarantees of forward invariance for the thrust-safety set. The authors provide a full dynamic model for a fully actuated hexacopter coupled with a robotic arm, a safety-analysis proving invariance, and extensive experiments comparing against baselines on static and dynamic interaction tasks, including scenarios with sudden changes in dynamics. The results demonstrate improved stability, robustness to disturbances, and repeatability in challenging APhI tasks without relying on force/torque sensing.

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.

Figures (8)

• Figure 1: Aerial physical interaction (APhI) experiments conducted for the validation of the proposed method. (a) Pushing a static structure. (b) Pulling an object from a static structure. (c) Pushing a movable object.
• Figure 2: An aerial manipulator configured with a fully actuated multirotor and a rigidly attached robotic arm. The thrust values $T_1, \cdots, T_6$ configure the thrust vector $\boldsymbol{T} \in {\mathbb{R}}^{6}$, and $\mathscr{F}_{W}$ and $\mathscr{F}_b$ represent the Earth-fixed and multirotor frames, respectively.
• Figure 3: Overall controller diagram for safe APhI
• Figure 4: Equipment utilized in the actual experiments: (a) a vertical whiteboard, (b) a frame with a 110 V electric socket, (c) a wheeled cart, and (d) an industrial fan.
• Figure 5: Histories of the vehicle's position ($\boldsymbol{p}$) and motor thrust values ($T_1,\cdots,T_6$) during the static whiteboard-pushing experiment with (a) the first baseline, (b) second baseline, and (c) the proposed method.

Theorems & Definitions (1)

• proof