Random Tree

Random trees, a class of tree-based structures, are used extensively in machine learning and robotics for tasks ranging from classification and regression to path planning and fault detection. Current research focuses on improving efficiency and accuracy, particularly through the development of novel algorithms like rapidly-exploring random trees (RRT) and their variants (e.g., PQ-RRT*, IRRT*), and integrating them with deep learning architectures for enhanced feature extraction and complex problem solving. These advancements are impacting diverse fields, enabling more efficient machine unlearning, improved fault detection in industrial processes, and more robust navigation systems for robots and autonomous vehicles.