The unprecedented growth of sensing, communications, and computation in the past few years has fundamentally changed the way we understand and process information. The vision of a multi-agent robotic network cooperatively learning and adapting in harsh unknown environments to achieve a common goal is closer than ever. In order to realize this vision, however, we need a foundational understanding of the interplay between sensing, communications and control in these systems. On the sensing side, a mobile network tasked with a certain exploratory mission faces an abundance of information. In such an information-rich world, there is simply not enough time to sample the whole environment. On the communication side, the communication between mobile agents can be severely degraded due to several propagation phenomena, making connectivity maintenance challenging. We then have the following important open question: what are the fundamentals of group decision making in these systems, so that the nodes can accurately build an understanding of the environment and accomplish the given task, despite limited sensing and communication?
In this talk, I develop a foundation for the integration of sensing, communication and navigation in mobile networks. Inspired by the recent results in non-uniform sampling theory, I show how a robotic network can exploit the sparse transformation of the parameter of interest for cooperative mapping based on only a small number of measurements. More specifically, I show that through proper motion design and by exploiting the sparsity of the map in another domain, it is indeed possible for the network to see through the walls and build a spatial map of occluded obstacles, using only a small number of wireless measurements. To ensure uninterrupted cooperation, I furthermore propose a new approach for communication-aware navigation and control in robotic networks. Along this line, I first show how each robot can assess the channel at unvisited locations through a probabilistic model-discovery and channel-prediction framework. I then show how this stochastic channel learning can be incorporated in robotic path planning, in order to ensure task accomplishment under resource constraints. The proposed framework is then validated with experimental results.
Yasamin Mostofi received the BS degree in electrical engineering from the Sharif University of Technology, Tehran, Iran, in 1997, and the MS and PhD degrees in the area of wireless communication systems from Stanford University, California, in 1999 and 2004, respectively. She is currently an assistant professor in the Department of Electrical and Computer Engineering at the University of New Mexico. Prior to that, she was a postdoctoral scholar in control and dynamical systems at the California Institute of Technology from 2004 to 2006.
Dr. Mostofi is the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and the National Science Foundation (NSF) CAREER award. She also received the Bellcore fellow-advisor award from the Stanford Center for Telecommunications in 1999. She won the 2008-2009 Electrical and Computer Engineering Distinguished Researcher Award from the University of New Mexico. Her research is on mobile sensor networks. Current research thrusts include communication-aware navigation and decision making in robotic networks, compressive sensing and control, obstacle mapping, robotic routers, and cooperative information processing. She has served on the Control Systems Society conference editorial board since 2008. She is a member of the IEEE.
