A Switched Adaptive Control Framework for Aerial Manipulators Under Dynamic Transitions
cs.RO
Submitted: 2026-09-21
Updated: 2026-10-03
Code: https://github.com/ethz-asl/mav
License: http://creativecommons.org/licenses/by/4.0/
The gist: Aerial manipulators represent the forefront of aerial robotics.
Terminology
Abstract
Aerial manipulators represent the forefront of aerial robotics. Although potentially capable of complex interaction tasks, controlling aerial manipulators throughout the dynamic transitions occurring during task execution presents significant challenges. Abrupt or discontinuous changes in system dynamics generated by the transitions suggest the use of a switched approach, yet the available aerial manipulation methods are not designed for coping with switched regimes. In addition, most available methods fall short in coping with the tight couplings between the aerial vehicle and the manipulator, as well as in coping with the state-dependent uncertainties arising from the difficulty in modeling such couplings. We propose a switched-based adaptive control framework for aerial manipulators not relying on a priori knowledge of the vehicle-manipulator couplings and of state-dependent uncertainties. To guarantee stable manipulation despite changes in system dynamics, the framework provides a class of switching signals characterizing those transition phases for which the system is guaranteed to remain stable. Comparative experiments further validate the effectiveness of the proposed switched-based framework over the state of the art.
Sources
- Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
- AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators
- Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators
- Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
- ARCADE: Adaptive Robot Control with Online Changepoint-Aware Bayesian Dynamics Learning
- AeroGrab: A Unified Framework for Aerial Grasping in Cluttered Environments
- AeroPlace-Flow: Language-Grounded Object Placement for Aerial Manipulators via Visual Foresight and Object Flow
- AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models
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