Efficient Path Planning for Differential Drive Mobile Robots Using the Rapidly-Exploring Random Tree (RRT) Algorithm

This study explores the application of the Rapidly-exploring Random Tree (RRT) algorithm for nonholonomic 3D path planning, leveraging 2D path planning methodologies as the foundation. The algorithm's ability to randomly sample and efficiently expand search trees is utilized to navigate complex environments with obstacles. Implemented using Matlab, the study evaluates the RRT algorithm across multiple scenarios with varying obstacle configurations, performing ten repetitions per scenario to optimize path planning. Results demonstrate RRT's superiority in generating shorter and more efficient paths compared to Dijkstra and A* algorithms, highlighting its capability to adapt to diverse constraints through random sampling. The findings underscore the algorithm’s critical role in autonomous navigation and engineering applications requiring precise, efficient path planning.