Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning
This addresses safety and efficiency for urban air mobility and UAS traffic management, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackled obstacle avoidance for unmanned aircraft by using deep reinforcement learning with Proximal Policy Optimization to enable continuous control, achieving a success rate of over 99% in simulations with static and moving obstacles.
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%.