Search and Rescue
Search and rescue (SAR) research focuses on developing autonomous robotic systems and advanced algorithms to improve the efficiency and effectiveness of locating and rescuing individuals in hazardous environments. Current research emphasizes the integration of heterogeneous robot teams (e.g., aerial and ground robots), leveraging advanced planning algorithms like model predictive control and reinforcement learning, often incorporating large language models for human-robot interaction and improved decision-making. These advancements aim to enhance situational awareness, optimize resource allocation, and ultimately improve the speed and success rate of SAR operations, impacting both disaster response and broader robotics applications.
Papers
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions
Zhixi Cai, Cristian Rojas Cardenas, Kevin Leo, Chenyuan Zhang, Kal Backman, Hanbing Li, Boying Li, Mahsa Ghorbanali, Stavya Datta, Lizhen Qu, Julian Gutierrez Santiago, Alexey Ignatiev, Yuan-Fang Li, Mor Vered, Peter J Stuckey, Maria Garcia de la Banda, Hamid Rezatofighi
Development and Testing of a Vine Robot for Urban Search and Rescue in Confined Rubble Environments
Zheyu Zhou, Yaqing Wang, Elliot W. Hawkes, Chen Li