Actuation Strategy

Actuation strategy research focuses on developing efficient and effective methods to control the movement and shape of robots and other dynamic systems. Current efforts explore diverse approaches, including model-based control with feedback mechanisms (e.g., PID controllers, Kalman filters), reinforcement learning algorithms for adapting to uncertain environments, and bio-inspired designs leveraging passive mechanisms or specialized actuator architectures (e.g., hydrostatic transmissions, swashplateless systems). These advancements are crucial for improving the performance, energy efficiency, and adaptability of robots in various applications, from aerial vehicles and legged locomotion to soft robotics and complex manipulators.