# Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions

arXiv: https://arxiv.org/abs/2003.02992 · HTML: https://mlsanity.com/paper/2003.02992/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 20 of 20)

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- [Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs](https://mlsanity.com/paper/2303.01931/) (2023)
- [Docking Multirotors in Close Proximity using Learnt Downwash Models](https://mlsanity.com/paper/2311.13988/) (2023)
- [A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation](https://mlsanity.com/paper/2401.03531/) (2024)
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- [Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness](https://mlsanity.com/paper/2112.09998/) (2021)
- [Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks](https://mlsanity.com/paper/2307.04374/) (2023)
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- [Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control](https://mlsanity.com/paper/2103.04548/) (2021)
- [Wake Up to the Past: Using Memory to Model Fluid Wake Effects on Robots](https://mlsanity.com/paper/2603.22472/) (2026)

## Abstract (arXiv)

In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-proximity flight of multirotor swarms. Close-proximity control is challenging due to the complex aerodynamic interaction effects between multirotors, such as downwash from higher vehicles to lower ones. Conventional methods often fail to properly capture these interaction effects, resulting in controllers that must maintain large safety distances between vehicles, and thus are not capable of close-proximity flight. Our approach combines a nominal dynamics model with a regularized permutation-invariant Deep Neural Network (DNN) that accurately learns the high-order multi-vehicle interactions. We design a stable nonlinear tracking controller using the learned model. Experimental results demonstrate that the proposed controller significantly outperforms a baseline nonlinear tracking controller with up to four times smaller worst-case height tracking errors. We also empirically demonstrate the ability of our learned model to generalize to larger swarm sizes.