Sampling Based Motion Planning

Sampling-based motion planning aims to efficiently find collision-free paths for robots navigating complex environments, addressing the challenge of high-dimensional configuration spaces. Current research focuses on improving sampling efficiency through informed heuristics, often leveraging graph neural networks and other machine learning techniques to guide exploration and generate more uniform sample distributions, as well as integrating these methods with other planning paradigms like Monte Carlo Tree Search. These advancements enhance the speed and robustness of motion planning algorithms, impacting applications ranging from robotics and autonomous driving to multi-robot coordination and environmental monitoring.