Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM). There are many techniques for real-time robust drone guidance, but many of them solve in discretized airspace and control, which would require an additional path smoothing step to provide flexible commands for UAS. To provide a safe and efficient computational guidance of operations for unmanned aircraft, we explore the use of a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to guide autonomous UAS to their destinations while avoiding obstacles through continuous control. The proposed scenario state representation and reward function can map the continuous state space to continuous control for both heading angle and speed. To verify the performance of the proposed learning framework, we conducted numerical experiments with static and moving obstacles. Uncertainties associated with the environments and safety operation bounds are investigated in detail. Results show that the proposed model can provide accurate and robust guidance and resolve conflict with a success rate of over 99%.

翻译：避免小型无人驾驶飞机出现障碍对于未来城市空中机动(UAM)和无人驾驶航空器系统交通管理(UAS)的安全至关重要。 使用许多实时强力无人机指导技术,但其中许多技术在离散的空气空间和控制中解决,这需要采取额外的平滑步骤,为无人机提供灵活的指令。为了对无人机的操作提供安全和高效的计算指导,我们探索使用基于Proximal政策优化(PPPO)的深度强化学习算法,引导自主的无人机系统前往目的地,同时通过持续控制避免障碍。拟议的假设状态代表制和奖励功能可以绘制连续状态空间图,以持续控制航向角度和速度。为了核查拟议的学习框架的性能,我们用静态和移动障碍进行了数字实验。详细调查了与环境和安全操作界限有关的不确定性。结果显示,拟议的模型能够提供准确和有力的指导,并解决成功率超过99%的冲突。