# Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation

arXiv: https://arxiv.org/abs/2203.02650 · HTML: https://mlsanity.com/paper/2203.02650/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024)
- [Collision Avoidance for Multiple UAVs in Unknown Scenarios with Causal Representation Disentanglement](https://mlsanity.com/paper/2407.04064/) (2024)
- [Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs](https://mlsanity.com/paper/2411.18009/) (2024)
- [Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2601.13657/) (2026)

## Abstract (arXiv)

Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems is considered a challenging task. It needs to ensure collision-free path planning in real-time, especially when the multi-UAV systems can become very crowded on certain occasions. In this paper, we presented a vision-based decentralized collision-avoidance policy for multi-UAV systems, which takes depth images and inertial measurements as sensory inputs and outputs UAV's steering commands. The policy is trained together with the latent representation of depth images using a policy gradient-based reinforcement learning algorithm and autoencoder in the multi-UAV threedimensional workspaces. Each UAV follows the same trained policy and acts independently to reach the goal without colliding or communicating with other UAVs. We validate our policy in various simulated scenarios. The experimental results show that our learned policy can guarantee fully autonomous collision-free navigation for multi-UAV in the three-dimensional workspaces with good robustness and scalability.