Markov Decision Processes in Autonomous Robot Navigation

This paper presents a comprehensive investigation of Markov Decision Processes (MDPs) in autonomous robot navigation, focusing on their application in dynamic and uncertain environments. MDPs provide a mathematical framework for modeling decision-making processes where outcomes are partially random and partially controlled by an autonomous agent. The research examines how MDPs enable robots to optimize their navigation strategies by balancing immediate rewards with long-term benefits, particularly in complex environments requiring real-time decision-making. The study implements a Reward-based Action Motion Deep Planning (RAMDP) system, integrating reinforcement learning techniques, specifically Q-learning, with traditional MDP frameworks. This integration enables robots to adapt their navigation strategies through experience and environmental feedback. The system's performance is evaluated across various environmental conditions, from simple to dynamic scenarios, measuring metrics including goal achievement rates, path optimization, and obstacle avoidance success. Results demonstrate significant improvements in navigation efficiency, with goal achievement rates maintaining above 80% even in dynamic environments. The system shows particular strength in real-time adaptability, effectively handling sudden obstacles and path blockages while maintaining energy efficiency. The implementation successfully balances computational efficiency with decision quality, making it suitable for real-world applications. This research contributes to the field of autonomous robotics by providing a robust framework for decision-making under uncertainty, offering practical solutions for challenges in robot navigation, and demonstrating the effectiveness of combining MDPs with modern machine learning techniques for improved autonomous navigation performance.