Model-Reference Reinforcement Learning for Safe Aerial Recovery of Unmanned Aerial Vehicles

Abstract

In this study, we propose an aerial rendezvous method to facilitate the recovery of unmanned aerial vehicles (UAVs) using carrier aircrafts, which is an important capability for the future use of UAVs. The main contribution of this study is the development of a promising method for online generation of feasible rendezvous trajectories for UAVs. First, the wake vortex of a carrier aircraft is analyzed using the finite element method, and a method for establishing a safety constraint model is proposed. Subsequently, a model-reference reinforcementearning algorithm is proposed based on the potential function method, which can ensure the convergence and stability of training. A combined reward function is designed to solve the UAV trajectory generation problem under non-convex constraints. The simulation results show that, compared with the traditional artificial potential field method under different working conditions, the success rate of this method under non-convex constraints is close to 100%, with high accuracy, convergence, and stability, and has greater application potential in the aerial recovery scenario, providing a solution to the trajectory generation problem of UAVs under non-convex constraints.

Keywords

• aerial UAV recovery
• wake vortices
• safety constraint model
• model-reference reinforcementearning