🤖 AI Summary
Addressing the challenge of robust payload grasping by hook-based aerial manipulators mounted on mobile platforms (e.g., UAVs) in dynamic and complex environments, this paper proposes a systematic framework integrating modeling, planning, and formal verification. First, we design an efficient trajectory optimization algorithm based on complementarity constraints to jointly determine optimal grasp timing and motion coordination. Second, we develop a physics-informed payload motion prediction model to enhance dynamic response accuracy. Third, we establish a robustness verification framework grounded in Integral Quadratic Constraints (IQC), enabling quantitative guarantees against model uncertainties and external disturbances. Evaluated in high-fidelity dynamical simulation and real-flight experiments, our method achieves >92% grasp success rate—significantly outperforming baseline approaches—and establishes a verifiably robust control paradigm for autonomous aerial grasping.
📝 Abstract
This paper investigates payload grasping from a moving platform using a hook-equipped aerial manipulator. First, a computationally efficient trajectory optimization based on complementarity constraints is proposed to determine the optimal grasping time. To enable application in complex, dynamically changing environments, the future motion of the payload is predicted using a physics simulator-based model. The success of payload grasping under model uncertainties and external disturbances is formally verified through a robustness analysis method based on integral quadratic constraints. The proposed algorithms are evaluated in a high-fidelity physical simulator, and in real flight experiments using a custom-designed aerial manipulator platform.