让无人机抓取物体更稳：通过视觉估惯性+抓后自适应
FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation
- 用视觉预估物体惯性，结合抓后调整策略
- 实测在负载变化下仍能稳定控制飞行与抓取
- 适合做空中作业的无人机系统研发者参考
空中机械臂（AMs）因相比传统多旋翼无人机具备更强灵活性，正日益受到自动化运输和应急服务领域的关注。然而，其实际部署面临时变惯性参数的复杂性挑战，这些参数对载荷变化和机械臂构型高度敏感。受人类交互未知物体策略启发，本文提出一种新型机载鲁棒空中操作框架。该系统集成基于视觉的抓取前惯性估计算法与抓取后自适应机制，实现惯性动态的实时估计与调节。控制方面，采用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其鲁棒性。研究为空中机械臂的抓取后控制提供了新见解，真实场景实验验证了所提框架的有效性与可行性。
原文摘要 · Abstract (English)
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.
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