Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning approach using PPO for obstacle avoidance in UAS, enabling continuous control for safe, flexible, and robust navigation in complex environments.

Contribution

It introduces a novel continuous control framework for UAS obstacle avoidance using deep RL with a specific state and reward design.

Findings

Success rate over 99% in obstacle avoidance tasks

Robust guidance in static and dynamic obstacle scenarios

Effective handling of environmental uncertainties

Abstract

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%.