Improved Q-Learning Algorithm Based on Flower Pollination Algorithm and Tabulation Method for Unmanned Aerial Vehicle Path Planning

Planning a path is crucial for safe and efficient Unmanned aerial vehicle flights, especially in complex environments. While the Q-learning algorithm in reinforcement learning performs better in handling such environments, it suffers from slow convergence speed and limited real-time capability. To address these problems, this study proposes an enhanced initialization process using the flower pollination algorithm and employs a tabulation method to improve local obstacle avoidance ability. An improved Q-learning algorithm based on the flower pollination algorithm and tabulation method (IQ-FAT) is proposed, which can perform both global and local path planning, enhance the convergence time of Q-learning, and expedite obstacle avoidance. Evaluation results on various obstacle maps demonstrate that the modified algorithm has a significant improvement convergence speed of approximately 40% compared to the original algorithm while enabling global path planning and local obstacle avoidance. Furthermore, the algorithm demonstrates superior path-planning capabilities in complex environments and enhances the dynamic response time of UAVs by approximately 90% compared to the artificial potential field method.