Path Planning under High-dimensional Input States Based on Deep Q-Network

Abstract

The field of autonomous navigation continues to face challenges in path planning, particularly when addressing the complex, high-dimensional input states that conventional algorithms struggle to process efficiently. This study presents a novel path-planning approach that utilizes Deep Q-Networks (DQN) to manage intricate and multidimensional environmental data. By integrating a DQN with path planning, this study aims to develop an adaptive system capable of making real-time decisions in dynamic environments. The methodology involves training a neural network to approximate the Q-function, enabling the agent to learn optimal strategies directly from unprocessed sensor data such as visual or LiDAR inputs. Experiments conducted in both simulated and real-world scenarios demonstrated the efficacy of this method, revealing significant improvements in route optimization, computational efficiency, and robustness against unforeseen obstacles compared to traditional techniques. The proposed system was evaluated in diverse settings, including urban environments and challenging terrain, illustrating its versatility. These findings suggest that DQN-based path planning has considerable potential for applications in robotics, autonomous vehicles, and other domains requiring intelligent decision-making under uncertainty.