Perception Based Control

Perception-based control integrates sensor data directly into control algorithms, aiming to create more robust and adaptable systems that react intelligently to their environment. Current research emphasizes developing safe and reliable control strategies using diverse sensor modalities (e.g., cameras, LiDAR, IMUs) and incorporating techniques like Control Barrier Functions (CBFs), Model Predictive Control (MPC), and deep reinforcement learning to handle uncertainty and ensure safety. This field is crucial for advancing autonomous systems in various domains, from robotics and UAVs to autonomous vehicles, by enabling more sophisticated and reliable interaction with complex, unpredictable environments. Addressing challenges like sensor noise, adversarial attacks, and efficient training of robust models remains a key focus.