Docking Multirotors in Close Proximity using Learnt Downwash Models

TL;DR

This paper presents a learned downwash model integrated into an optimal control framework to enable precise vertical docking of multirotors in close proximity, significantly reducing tracking errors and achieving successful physical docking.

Contribution

It introduces an online learned downwash model for multirotor docking, improving close-proximity flight accuracy and enabling successful in-air docking maneuvers.

Findings

Tracking error reduced to less than 0.06m

Achieved successful physical docking in real-world tests

Demonstrated importance of downwash compensation for close proximity flight

Abstract

Unmodeled aerodynamic disturbances pose a key challenge for multirotor flight when multiple vehicles are in close proximity to each other. However, certain missions \textit{require} two multirotors to approach each other within 1-2 body-lengths of each other and hold formation -- we consider one such practical instance: vertically docking two multirotors in the air. In this leader-follower setting, the follower experiences significant downwash interference from the leader in its final docking stages. To compensate for this, we employ a learnt downwash model online within an optimal feedback controller to accurately track a docking maneuver and then hold formation. Through real-world flights with different maneuvers, we demonstrate that this compensation is crucial for reducing the large vertical separation otherwise required by conventional/naive approaches. Our evaluations show a tracking error of less than 0.06m for the follower (a 3-4x reduction) when approaching vertically within two body-lengths of the leader. Finally, we deploy the complete system to effect a successful physical docking between two airborne multirotors in a single smooth planned trajectory.