Dr. Stephanie Gil, Arizona State University: Trusted Autonomy and Communication in Multi-Robot Systems via Coordinated Control
Location: Hibiscus B
Date/Time: Tuesday, Sep. 17, 9:00 a.m.
Robust information exchange and trusted coordination are both critical needs for multi-robot systems acting in the real world. While these needs are universal across platforms, the computing and sensing resources of these platforms are not – making effective coordination difficult to enable, to scale, and to secure. This talk will present new methods of trusted autonomy and adaptive network formation for resource-constrained, mobile multi-robot systems (applications include delivery drones, mobile IoT, and robotic vehicles). We develop a theoretical and experimental framework for provably securing multi-robot distributed algorithms using communicated wireless signals. In particular, we build off of our developed technologies in 1) position control algorithms for allowing multiple robots to achieve high data rate networks and 2) sensing over wireless communication channels between robots, in order to enable and secure various multi-agent tasks such as consensus, coverage, and drone delivery. We will place a particular focus on our most recent results in securing multi-agent consensus as well as distributed mapping and pose estimation.
Stephanie Gil is an Assistant Professor in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University (Jan 2018). Her work centers around trust and coordination in multi-robot systems for which she has been granted an NSF CAREER award (see Improving Mission Intelligence within Fleets of Robots) and has been reviewed in MIT News (see some of her work in security for multi-robot systems and human-robot EEG based communication) as well as several other news outlets including Forbes and the Financial Times (full list on her website). Prior, she was a research scientist in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT where she also completed her Ph.D. work (2014) on multi-robot coordination and control and M.S. work (2009) on system identification and model learning. At MIT she collaborated extensively with the wireless communications group NetMIT, the result of which were two U.S. patents recently awarded in adaptive heterogeneous networks for multi-robot systems and accurate indoor positioning using Wi-Fi. She completed her B.S. at Cornell University in 2006.