FADQN: A Heuristic Reinforcement Learning Mechanism for UAV Path Planning in Unknown Environment

Path planning remains a focal point in Unmanned Aerial Vehicle (UAV) research, with autonomous path planning in unknown environments emerging as a particularly active area. Deep Reinforcement Learning (DRL) has become a standard approach for addressing path planning challenges in such environments. However, traditional DRL algorithms often suffer from slow convergence. To address this issue, this paper introduces an enhanced deep reinforcement learning algorithm, termed Firefly Algorithm-enhanced Deep Q-Network (FADQN). In FADQN, the action output from the Firefly Algorithm is integrated into the Deep Q-Network to guide early action selection, thereby accelerating convergence. To balance exploration and exploitation, an adaptive $\varepsilon $ -greedy strategy is proposed. Furthermore, a prioritized experience replay mechanism is employed to increase the likelihood of the agent learning from critical experiences. Simulation experiments demonstrate that FADQN achieves shorter paths (reducing the path length by 7.32% in complex environments), fewer turns, faster and more stable convergence, and higher success rates across various environments compared to traditional DQN. These results validate the effectiveness of FADQN in solving UAV path planning problems.