As physical robot networks become more pervasive all around us, in the form of teams of autonomous vehicles, fleets of delivery drones, and smart and mobile IoT, it becomes increasingly critical to question the robustness of their coordination algorithms to security threats and/or corrupted data. Indeed, it has been shown that many multi-robot tasks easily fail in the presence of erroneous or hacked data. We investigate the vulnerabilities of important multi-robot algorithms such as consensus, coverage, and distributed mapping to malicious or erroneous data and we demonstrate the potential of communication to thwart certain attacks, such as the Sybil Attack, on these algorithms. Our key insight is that coordinated mobility can be combined with signal processing of communication signals to allow agents to learn important information about the environment and the nature of other agents in the network (for example the presence of cooperative versus adversarial agents). Along these lines, we will present a theoretical and experimental framework for provably securing multi-robot distributed algorithms through careful use of communication. We will present both theoretical results and experimental results on actual hardware implementations for bounding the influence of a Sybil Attack on consensus and on coverage by using observations over the wireless channels. In some cases, we show that the effect of a Sybil Attack can be nearly eliminated with high probability by deriving the appropriate switching function using a sufficient number of observations over the wireless network. Finally, we will briefly describe promising results on new methods for outlier rejection and active rendezvous in a pose graph optimization framework that exploits feedback gathered from communication channels to arrive at improved accuracy.
Stephanie is currently visiting Stanford for the summer term. She 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.