Trajectory Planning

Trajectory planning focuses on generating optimal paths for robots and autonomous vehicles, considering factors like speed, acceleration, and collision avoidance. Current research emphasizes robust methods handling uncertainties in dynamic environments, employing techniques such as Partially Observable Markov Decision Processes (POMDPs), Bayesian games, and neural networks (including transformers and graph neural networks) for improved prediction and decision-making. These advancements are crucial for enhancing the safety, efficiency, and reliability of autonomous systems across diverse applications, from autonomous driving and multi-robot coordination to teleoperated space manipulators and advanced robotics.