TRUST-UP: Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuit

Reinforcement Learning (RL) enables autonomous aerial vehicles to adapt quickly and make efficient decisions, making it well-suited for dynamic urban air mobility operations. However, the lack of safety guarantees and transparency hinders the airworthiness certification of RL-based flight control systems, particularly in low-altitude urban environments with human presence. This paper proposes a trustworthy reinforcement learning algorithm that utilizes safe techniques to address the AI trustworthiness requirements for aviation safety, ensuring the transparent and certifiable deployment of RL in safety-critical aerial operations. Specifically, we proposed a Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuit (TRUST-UP), which consists of two key components: a safety filter constructed from Control Barrier Functions (CBFs) that transforms unsafe RL actions into provably safe flight commands, and a switching strategy that enhances feasibility while maintaining operational transparency. These components enable trustworthy AI deployment in urban airspace, satisfying technical robustness and transparency requirements for aviation certification. Simulation results demonstrate that TRUST-UP enables autonomous UAVs to safely navigate congested urban environments while maintaining human-interpretable decision logic. This work contributes toward certifiable and explainable AI frameworks for low-altitude aviation, addressing the critical need for trustworthy autonomous flight systems in future urban air mobility.