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

TL;DR

This paper introduces a decentralized, vision-based collision avoidance policy for multi-UAV systems using deep reinforcement learning, enabling autonomous, collision-free navigation in complex 3D environments without communication.

Contribution

It presents a novel decentralized collision avoidance method that uses depth images and inertial data with reinforcement learning, trained via autoencoders for multi-UAV navigation.

Findings

Guarantees collision-free navigation in 3D environments

Demonstrates robustness and scalability in various scenarios

Operates without inter-UAV communication

Abstract

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.