Special Seminar: “Density-Driven Control of an Autonomous Multi-Agent System”

Abstract:

Multi-agent systems (or multi-robot systems) have great potential to be applied to wide applications since they can provide many advantages over a single agent such as time efficiency while being less prone to a single point of failure. For instance, multi-agent systems can be used for search and rescue, surveillance and reconnaissance, smart farming, large-scale infrastructure inspections, wildlife monitoring for endangered species, and planetary explorations. In these missions, efficiency will likely be the key element because the size of the domain (or quantity of interest) for these missions is generally huge whereas the number of agents and their energies are finite, necessitating a transformative idea to cover areas of interest efficiently rather than uniformly. As a result, there is a need to develop a new multi-agent control algorithm to maximize multi-agent coverage efficiency while providing a holistic approach for collaboration between multiple agents. This seminar introduces a density-driven multi-agent exploration scheme through the Optimal Transport theory for such efficiency. This work is based on our research group’s recent works to develop a new multi-agent control algorithm. By taking into account a probability density function (PDF) given as reference density, this scheme will bring a novel idea to multi-agent coverage problems. 

Bio:

Dr. Kooktae Lee received his B.S. and M.S. degrees in the Department of Mechanical Engineering from Korea University in 2006 and 2008, respectively. He received his Ph.D. degree in the Department of Aerospace Engineering from Texas A&M University in 2015 and continued his research as a postdoctoral research associate from 2015 to 2016. He was a postdoctoral scholar in the Department of Mechanical & Aerospace Engineering at the University of California San Diego from 2016 to 2017. Since 2017, he is an Assistant Professor with the Department of Mechanical Engineering at New Mexico Institute of Mining and Technology. Dr. Lee received the NSF CAREER Award in 2022 and his major research interests include robotics and control, multi-agent systems, distributed networked control systems, uncertainty propagation/quantification, stochastic switched systems, and asynchronous algorithms.