Sampling Based Motion Planner

Sampling-based motion planning aims to efficiently generate collision-free paths for robots navigating complex environments, focusing on improving speed, path quality, and robustness to uncertainties. Current research emphasizes integrating machine learning techniques, such as transformers and neural networks, to learn efficient sampling strategies and improve collision detection, often leveraging architectures like Rapidly Exploring Random Trees (RRTs) and Gaussian Processes (GPs). These advancements are crucial for enabling real-time robot control in dynamic, high-dimensional spaces and for applications ranging from autonomous driving to surgical robotics.