Research Projects

Distributed and Collaborative Intelligent Systems and Technology (ARL CRA DCIST)

The Distributed and Collaborative Intelligent Systems and Technology (DCIST) Collaborative Research Alliance (CRA) will create Autonomous, Resilient, Cognitive, Heterogeneous Swarms that can enable humans to participate in wide range of missions in dynamically changing, harsh and contested environments. These include search and rescue of hostages, information gathering after terrorist attacks or natural disasters, and humanitarian missions. Teams of humans and robots will operate as a cohesive team with robots preventing humans from coming in harms way (Force Protection) and extending and amplifying their reach to allow 1 human to do the work of 10 humans (Force Multiplication). The team is led by the University of Pennsylvania and includes collaborators from the U.S. Army Research Laboratory, Massachusetts Institute of Technology, Georgia Institute of Technology, University of California and University of Southern California.


Lyapunov-Certified Cognitive Control for Safe Autonomous Navigation in Unknown Environments (NSF CRII RI)

Applications for unmanned aerial and ground vehicles requiring autonomous navigation in unknown, cluttered, and dynamically changing environments are increasing in fields such as transportation, delivery, agriculture, environmental monitoring, and construction. To achieve safe, resilient, and self-improving autonomous navigation, this project focuses on the design of adaptive online environment understanding and Lyapunov-theoretic control techniques to guarantees stable and collision-free operation in challenging conditions. This research direction is important because current practices rely on prior or hand-crafted maps that attempt to capture the whole environment, even if parts are irrelevant for specific navigation tasks. This increases memory and computation requirements, spreads the effects of noise, and makes current approaches brittle, particularly in conditions involving dynamic obstacles, unreliable localization, or illumination variation.