Multi-agent systems, which distribute information and action over many interacting units, are increasingly common in engineering applications. As a result, engineers across disciplines are challenged to design control strategies to enable teams to achieve common goals. Such large-scale coordination tasks are well executed in nature by animal groups exhibiting collective behavior, such as fish schools and bird flocks, where complex structures emerge from decisions based on local information without a centralized leader. Among these groups, bat swarms stand out as a unique
example, since they can perform sophisticated active sensing (echolocation) for navigation and hunting which is intercepted and may be used by their peers. Unlike groups that only use passive sensing such as vision, multi-agent systems inspired by bat swarms may be able to achieve types of collective behavior applicable to engineered systems using active sensing, such as robotic teams using sonar or lidar. In this talk, we first explore data-driven techniques that can be used to uncover interactions between bats flying together in the wild. We then identify challenges in writing mathematical models for multi-agent systems that use passive and active sensing, and look in detail at how such sensor fusion may be achieved for individual systems tackling the canonical robotics problem of simultaneous localization and mapping or SLAM.
Nicole Abaid received her bachelor’s and master’s degrees in mathematics from the University of North Carolina at Chapel Hill and the University of Kansas, respectively, and her Ph.D. in mechanical engineering from the Polytechnic Institute of New York University in 2012. Until 2019, she worked as an assistant professor in the Department of Biomedical Engineering and Mechanics at Virginia Tech (formerly the Department of Engineering Science and Mechanics). Since then, she has been an associate professor in the Department of Mathematics at Virginia Tech. Her research interests include on dynamic systems, animal behavior, and bio-inspired robotics.