Mission Related Maneuverability Analysis

Mission-related maneuverability analysis focuses on optimizing the movement and control of robots and vehicles to successfully complete tasks in challenging environments. Current research emphasizes developing advanced control algorithms, such as model predictive control and path integral methods, often incorporating machine learning techniques like deep Koopman operators and Gaussian processes to handle uncertainties and improve robustness. These advancements are crucial for enhancing the safety and efficiency of autonomous systems in various applications, including navigation in cluttered spaces, evasive maneuvers, and agile operations in air, water, and on land.