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

2026-01-08

AIAA SCITECH 2026 Forum

AI Summary

This work addresses the challenge of precise control for aerial manipulation platforms operating in real-world wind fields and near-wall environments, where complex aerodynamic effects are often neglected. To overcome this limitation, the authors propose a unified control framework that integrates a physics-based blade element model with a learning-based residual force estimator. The approach uniquely combines high-fidelity aerodynamic modeling with online adaptive learning through real-time rotor speed allocation and an online parameter adaptation mechanism. Experimental results demonstrate significant improvements in disturbance estimation accuracy, trajectory tracking performance, and robustness under previously unseen wind disturbances and near-wall conditions. Notably, the method enables stable physical contact with vertical surfaces even under strong wind perturbations, marking a critical advancement toward reliable aerial manipulation in complex, real-world settings.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• aerial manipulation
• wind disturbances
• near-wall aerodynamics
• disturbance compensation
• unmodeled aerodynamic effects

Innovation

• physics-informed learning
• aerial manipulation
• blade-element model
• residual force estimation
• online adaptation