A UAV Coverage Path Planning Algorithm Based on Double Deep Q-Network

Abstract

The UAV path planning method has practical value in the military field and automated production. Based on deep reinforcement learning theory and the characteristics of coverage path planning, this paper designs and implements a set of deep reinforcement learning frameworks suitable for UAV coverage path planning and trains it in the abstract environment model built. The simulation experiment results show that the designed UAV coverage path planning frameworks can consider obstacles, no-fly zones and length constraints, plan a reasonable path to complete the coverage task, and have a certain generalization ability.