  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.
