Dr. Dylan Shell, assistant professor in the Department of Computer Science and Engineering (CSE) at Texas A&M University, received a CAREER grant from the NSF Information & Intelligent Systems' Robust Intelligence Program to investigate the limitations of traditional ways of programming groups of robots to cooperate in order to solve problems together. The grant for "CAREER: Bridging Self-Organized and Algorithmic Approaches to Multi-Robot Systems," is funded till the end of February 2020.
"This research is establishing new connections between methods developed for thinking about very large data, mathematical models invented by physicists for small-scale phenomena, and today's robot swarms," said Shell. "It will help realize a future where robots address important applications such as those with life-saving, ecological, and national strategic elements (e.g., manufacturing, roboticized agriculture, planetary exploration)."
Asked to explain the impetus for this particular investigation, Shell responded "over the last couple of decades, two disparate perspectives have come to dominate thinking about multi-robot systems, each perspective or paradigm having its own philosophy, tools, models, and even publication venues. The idea being explored by this research is that the existing separation of the paradigms is vestigial, arising out of early AI questions about representation, and that for progress to be made it is essential that methods and tools accommodate systems that mix the characteristics of both paradigms. The work will improve scalability, performance, robustness, and model predictability for multi-robot systems by bridging and consolidating the paradigms."
After receiving his bachelor's degree in computational and applied mathematics and computer science from the University of the Witwatersrand in South Africa and his master's and doctoral degrees in computer science from the University of Southern California, Shell joined the faculty at Texas A&M. His research interests are in the areas of distributed AI, biologically-inspired multi-robot systems, coordinated system, analysis of multi-agent systems, and crowd modeling. He is very active in his field, publishing at competitive conferences in robotics each year.
Shell was recognized as a Montague-CTE (Center for Teaching Excellence) Scholar in 2013 for excellence in undergraduate teaching, and received the CSE department's Faculty Service Excellence Award in 2010. He teaches graduate and undergraduate courses in artificial intelligence, multi-robot systems, and system design.