Dynamic Path Planning Algorithm for Mobile Robots: Leveraging Reinforcement Learning for Efficient Navigation

Traversing unfamiliar terrain presents a considerable challenge, particularly concerning the task of locating a viable pathway, regardless of its actual existence. This paper presents a novel navigation algorithm leveraging reinforcement learning, specifically the Markov Decision Process, to address the challenges of navigating dynamic environments. In contrast to traditional methods, this approach offers adaptability and efficiency in scenarios ranging from mobile robot navigation to complex industrial settings. The algorithm integrates an enhanced A* algorithm, showcasing its versatility in handling various tasks, from pathfinding to obstacle avoidance. To evaluate its effectiveness, the algorithm undergoes rigorous testing across multiple scenarios, comparing its performance with and without reinforcement learning. Through extensive experimentation, the algorithm demonstrates superior performance in terms of efficiency and adaptability, particularly in scenarios. The results presented highlight the algorithm's learning progress and effectiveness in finding the shortest path. Notably, the algorithm's performance surpasses that of conventional approaches, underscoring its potential for real-world applications in mobile robot navigation and beyond. In conclusion, the proposed algorithm represents a significant advancement in navigation techniques, offering a robust solution for addressing the challenges posed by dynamic environments. Its integration of reinforcement learning enhances adaptability and efficiency, making it a promising tool for various industries and applications.