Motion Planner

Motion planning algorithms aim to generate safe and efficient trajectories for robots navigating complex environments, addressing challenges like high-dimensionality, dynamic obstacles, and complex robot kinematics. Current research emphasizes integrating advanced models such as graph neural networks (GNNs) and large language models (LLMs) to improve planning efficiency and robustness, often incorporating techniques like sampling-based planning, model predictive control (MPC), and hierarchical approaches. These advancements are crucial for enabling autonomous systems in diverse applications, from autonomous driving and surgical robotics to underwater aquaculture and collaborative manipulation tasks, improving safety, efficiency, and overall system performance.