High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning

TL;DR

This paper introduces a hybrid reinforcement learning framework for quadrotor UAVs that combines physics-informed training and real-time safety filtering, enabling safe, high-speed navigation in cluttered environments.

Contribution

It presents a novel end-to-end RL approach with integrated safety mechanisms, bridging the gap between fast autonomous flight and formal safety guarantees.

Findings

Outperforms traditional planners and recent RL methods in obstacle avoidance.

Enables reliable high-speed navigation up to 7.5 m/s in dense clutter.

Demonstrates strong generalization in outdoor forest environments.

Abstract

Quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in complex missions that demand reliable autonomous navigation and robust obstacle avoidance. However, traditional modular pipelines often incur cumulative latency, whereas purely reinforcement learning (RL) approaches typically provide limited formal safety guarantees. To bridge this gap, we propose an end-to-end RL framework augmented with model-based safety mechanisms. We incorporate physical priors in both training and deployment. During training, we design a physics-informed reward structure that provides global navigational guidance. During deployment, we integrate a real-time safety filter that projects the policy outputs onto a provably safe set to enforce strict collision-avoidance constraints. This hybrid architecture reconciles high-speed flight with robust safety assurances. Benchmark evaluations demonstrate that our method outperforms both traditional planners and recent end-to-end obstacle avoidance approaches based on differentiable physics. Extensive experiments demonstrate strong generalization, enabling reliable high-speed navigation in dense clutter and challenging outdoor forest environments at velocities up to 7.5m/s.