FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation

TL;DR

This paper introduces a novel onboard framework for aerial manipulators that combines vision-based inertia estimation with post-grasp adaptation, enhancing robustness and control during payload variations in real-time operations.

Contribution

It presents a new integrated system with inertia-aware control and real-time estimation, addressing challenges of payload variability in aerial manipulation.

Findings

Effective real-time inertia estimation using vision-based methods

Improved robustness of control through gain scheduling

Successful validation in real-world experiments

Abstract

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.