Safe Motion

Safe motion research focuses on developing algorithms and control strategies that enable robots and autonomous systems to operate safely in dynamic and uncertain environments, prioritizing collision avoidance and adherence to operational constraints. Current research emphasizes robust control methods like Model Predictive Control (MPC) and Control Barrier Functions (CBFs), often integrated with advanced state estimation techniques and uncertainty modeling (e.g., using stochastic signed distance functions or reachability analysis) to handle unpredictable behaviors and sensor noise. These advancements are crucial for enabling safe human-robot interaction, reliable autonomous navigation, and the broader deployment of robots in unstructured settings, impacting fields from manufacturing and logistics to healthcare and personal assistance.