Ga-DQN: A Gravity-aware DQN Based UAV Path Planning Algorithm

Abstract

Unmanned aerial vehicles (UAVs) path planning refers to exploring the optimal flight trajectory from the starting point to the destination that satisfies the UAV under specific constraints such as maneuverability and environmental information constraints, which is a crucial technology for the UAV mission planning. In order to enhance the efficiency and safety of the UAV path planning task, a new autonomous UAV path planning system based on deep reinforcement learning is proposed in this article. At the beginning, a new action guidance strategy based on the Deep Q-Network (DQN) algorithm is introduced via deploying the Gravity-aware Deep Q-Network (Ga-DQN) method. This strategy can effectively assist the UAVs to avoid the obstacles in the specific state. For balancing the efficiency and safety of the task, a reward scheme that introduces a safety counting mechanism is proposed to provide global guidance for the agent in Deep Reinforcement Learning (DRL). The simulation results under different obstacle densities show that the proposed novel strategy can obviously behave robust and greater efficiency compared to the traditional methods.