Aerial manipulators (AMs) are gaining increasing attention in automated transportation and emergency services due to their superior dexterity compared to conventional multirotor drones. However, their practical deployment is challenged by the complexity of time-varying inertial parameters, which are highly sensitive to payload variations and manipulator configurations. Inspired by human strategies for interacting with unknown objects, this letter presents a novel onboard framework for robust aerial manipulation. The proposed system integrates a vision-based pre-grasp inertia estimation module with a post-grasp adaptation mechanism, enabling real-time estimation and adaptation of inertial dynamics. For control, we develop an inertia-aware adaptive control strategy based on gain scheduling, and assess its robustness via frequency-domain system identification. Our study provides new insights into post-grasp control for AMs, and real-world experiments validate the effectiveness and feasibility of the proposed framework.

翻译：空中机械臂因其相比传统多旋翼无人机更优越的灵巧性，在自动化运输与应急服务领域受到日益广泛的关注。然而，其时变惯性参数的复杂性对其实际部署构成了挑战，这些参数对负载变化和机械臂构型高度敏感。受人类与未知物体交互策略的启发，本文提出了一种用于鲁棒空中操控的新型机载框架。该系统将基于视觉的预抓取惯性估计模块与抓取后适应机制相结合，实现了惯性动力学的实时估计与适应。在控制方面，我们开发了一种基于增益调度的惯性感知自适应控制策略，并通过频域系统辨识评估了其鲁棒性。本研究为空中机械臂的抓取后控制提供了新的见解，实际实验验证了所提框架的有效性与可行性。