mlsanityData to 14 Oct 2026

Docking Multirotors in Close Proximity using Learnt Downwash Models

arXiv 2311.13988 ↗First posted 23 Nov 2023cs.RO cs.LG cs.SYcited by 2

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Abstract (arXiv)

Unmodeled aerodynamic disturbances pose a key challenge for multirotor flight when multiple vehicles are in close proximity to each other. However, certain missions textitrequire 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

5 of 12 references.

References · 5

  1. 2022Sample Efficient Grasp Learning Using Equivariant Modelscited by 53
  2. 2020Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactionscited by 20
  3. 2023SO(2)-Equivariant Downwash Models for Close Proximity Flightcited by 7
  4. 2023Nonlinear MPC for Quadrotors in Close-Proximity Flight with Neural Network Downwash Predictioncited by 6
  5. 2020Docking two multirotors in midair using relative vision measurementscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.