Motion Planning Framework

Motion planning frameworks aim to generate safe and efficient trajectories for robots and autonomous vehicles navigating complex environments, often incorporating high-level task specifications. Current research emphasizes integrating diverse techniques, such as model predictive control (MPC), Monte Carlo tree search (MCTS) enhanced by reinforcement learning, and optimization-based methods informed by signal temporal logic (STL) or game theory, to handle uncertainty, dynamic obstacles, and multi-agent interactions. These advancements are crucial for enabling safe and robust autonomy in applications ranging from search and rescue robotics to autonomous driving and industrial automation.