Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces
Yadav, Rishabh Dev, Dantu, Swati, Pan, Wei, Sun, Sihao, Roy, Spandan, Baldi, Simone
–arXiv.org Artificial Intelligence
--Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator . However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. I. INTRODUCTION An Unmanned Aerial Manipulator (UAM) is a coupled system where a quadrotor (or multirotor) vehicle carries a manipulator: the presence of the manipulator greatly improves the dexterity and flexibility of the quadrotor, making it capable to accomplish a wide range of tasks, from simple payload transportation to more complex tasks such as pick and place, contact-based inspection, grasping and assembling etc. [1]-[8]. This work was supported in part by "Aerial Manipulation" under IHFC grand project (GP/2021/DA/032), in part by "Capacity building for human resource development in Unmanned Aircraft System (Drone and related Technology)", MeiTY, India, in part by the Natural Science Foundation of China grants 62233004 and 62073074, and in part by Jiangsu Provincial Scientific Research Center of Applied Mathematics grant BK20233002.
arXiv.org Artificial Intelligence
Oct-10-2024
- Genre:
- Research Report > Promising Solution (0.34)
- Industry:
- Aerospace & Defense > Aircraft (0.34)
- Energy (0.88)
- Information Technology > Robotics & Automation (0.34)
- Technology:
- Information Technology > Artificial Intelligence > Robots > Autonomous Vehicles > Drones (0.48)