Reinforcement Learning-Based Air Traffic Deconfliction
This work addresses safety-critical obstacle avoidance for uncrewed aerial vehicles, representing a domain-specific incremental advancement.
The paper tackled the problem of automating horizontal separation for uncrewed aerial vehicles to ensure safety in congested airspace, resulting in a system that generates quick and achievable avoidance trajectories validated through high-fidelity simulation and full-scale demonstration.
Remain Well Clear, keeping the aircraft away from hazards by the appropriate separation distance, is an essential technology for the safe operation of uncrewed aerial vehicles in congested airspace. This work focuses on automating the horizontal separation of two aircraft and presents the obstacle avoidance problem as a 2D surrogate optimization task. By our design, the surrogate task is made more conservative to guarantee the execution of the solution in the primary domain. Using Reinforcement Learning (RL), we optimize the avoidance policy and model the dynamics, interactions, and decision-making. By recursively sampling the resulting policy and the surrogate transitions, the system translates the avoidance policy into a complete avoidance trajectory. Then, the solver publishes the trajectory as a set of waypoints for the airplane to follow using the Robot Operating System (ROS) interface. The proposed system generates a quick and achievable avoidance trajectory that satisfies the safety requirements. Evaluation of our system is completed in a high-fidelity simulation and full-scale airplane demonstration. Moreover, the paper concludes an enormous integration effort that has enabled a real-life demonstration of the RL-based system.