Physics-infused Learning for Aerial Manipulator in Winds and Near-Wall Environments

TL;DR

This paper introduces a physics-informed learning framework for aerial manipulators operating in windy and near-wall environments, combining aerodynamic modeling with learning-based residual estimation for improved disturbance compensation.

Contribution

It presents a novel integrated control approach that combines a physics-based blade-element model with a learning residual estimator and online adaptation for robust UAV manipulation near walls.

Findings

Enhanced disturbance estimation accuracy.

Improved trajectory tracking in complex aerodynamic conditions.

Successful wall-contact tasks in simulated environments.

Abstract

Aerial manipulation (AM) expands UAV capabilities beyond passive observation to contact-based operations at high altitudes and in otherwise inaccessible environments. Although recent advances show promise, most AM systems are developed in controlled settings that overlook key aerodynamic effects. Simplified thrust models are often insufficient to capture the nonlinear wind disturbances and proximity-induced flow variations present in real-world environments near infrastructure, while high-fidelity CFD methods remain impractical for real-time use. Learning-based models are computationally efficient at inference, but often struggle to generalize to unseen condition. This paper combines both approaches by integrating a physics-based blade-element model with a learning-based residual force estimator, along with a rotor-speed allocation strategy for disturbance compensation, resulting in a unified control framework. The blade-element model computes per-rotor aerodynamic forces under wind and provides a refined feedforward disturbance estimate. A learning-based estimator then predicts the residual forces not captured by the model, enabling compensation for unmodeled aerodynamic effects. An online adaptation mechanism further updates the residual-force prediction and rotor-speed allocation jointly to reduce the mismatch between desired and realized thrust. We evaluate this framework in both free-flight and wall-contact tracking tasks in a simulated near-wall wind environment. Results demonstrate improved disturbance estimation and trajectory-tracking accuracy over conventional approaches, enabling robust wall-contact execution under challenging aerodynamic conditions.