Rapidly Exploring Random Tree

Rapidly-exploring Random Trees (RRTs) are probabilistic algorithms used for path planning in complex environments, primarily aiming to efficiently find collision-free paths for robots and other autonomous systems. Current research focuses on improving RRT's efficiency and optimality through enhancements like informed sampling strategies, integration with other planning methods (e.g., Gaussian Processes, Model Predictive Path Integral), and the development of variants such as RRT*, Bi-directional RRT, and Batch Informed Trees (BIT*). These advancements are significant for applications ranging from autonomous navigation in challenging terrains and automated parking to multi-robot coordination and motion planning for complex systems like floating-base manipulators, improving both speed and path quality.