Research on Intelligent Navigation and Path Planning of Mobile Robot Based on RRT

Abstract

This study investigates the Rapidly-exploring Random Tree (RRT) algorithm's efficacy in mobile robot navigation, focusing on intelligent path planning amidst dynamic, high-dimensional environments. While RRT's foundational principles facilitate quick exploration of state spaces, its adaptability to evolving conditions and compatibility with advanced sensing technologies remain underexplored. This research delves into the RRT's core functionalities and extends its analysis to various enhanced iterations like RRT* and I-RRT*, showcasing their increased efficiency in real-world applications such as autonomous vehicle navigation and robotic manipulator path planning. The paper critically assesses the RRT's algorithmic structure, emphasizing its strategic growth in uncharted territories and its application in navigating through environments laden with static and dynamic obstacles. Through a series of case studies, the paper illustrates the algorithm's real-time responsiveness and its ability to synthesize with genetic algorithms and neural networks for optimal path determination. Prospects of the RRT algorithm are explored, suggesting its integration with AI and machine learning to augment path planning intelligence. The study posits that such integration will lead to more robust and adaptive navigational strategies, catering to the intricate demands of modern automated systems. Concluding, this research elucidates the RRT algorithm's current state, potential enhancements, and future trajectory, offering a pivotal reference for the development of more sophisticated autonomous navigation systems.