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.

翻译：四旋翼无人机在复杂任务中的部署日益增多，这些任务要求可靠的自主导航和鲁棒的避障能力。然而，传统的模块化流水线通常会产生累积延迟，而纯粹的强化学习方法通常只能提供有限的形式化安全保证。为弥合这一差距，我们提出了一种结合基于模型安全机制的端到端强化学习框架。我们在训练和部署阶段均融入了物理先验知识。在训练阶段，我们设计了一种基于物理信息的奖励结构，以提供全局导航指导。在部署阶段，我们集成了一个实时安全滤波器，将策略输出投影到一个可证明的安全集合上，以强制执行严格的避障约束。这种混合架构实现了高速飞行与鲁棒安全保证的协调。基准评估表明，我们的方法优于传统规划器以及近期基于可微分物理的端到端避障方法。大量实验证明了其强大的泛化能力，能够在密集杂乱环境和具有挑战性的室外森林环境中，以高达7.5米/秒的速度实现可靠的高速导航。