Dynamic Maneuver

Dynamic maneuver research focuses on enabling autonomous systems, including vehicles and robots, to execute complex and agile movements efficiently and safely. Current efforts concentrate on developing robust control algorithms, often leveraging model predictive control (MPC) and deep reinforcement learning (RL), alongside advanced modeling techniques like pseudospectral collocation and vortex particle models to handle complex dynamics and uncertainties. These advancements are crucial for improving autonomous navigation in challenging environments, enhancing the capabilities of robots for tasks like orbital debris removal and powerline inspection, and enabling safer and more efficient automated driving systems.