Shirantha Welikala
Current Institution: University of Notre Dame
Email: wwelikal@nd.edu
Bio: Shirantha Welikala received the B.Sc. degree in Electrical and Electronic Engineering from the University of Peradeniya, Peradeniya, Sri Lanka, in 2015 and the M.Sc. and the Ph.D. degrees in Systems Engineering from Boston University, Brookline, MA, USA, in 2019 and 2021, respectively. From 2015-2017, he was with the Department of Electrical and Electronic Engineering, University of Peradeniya, where he worked as a Temporary Instructor and a Research Assistant. He is currently a Postdoctoral Researcher in the Department of Electrical Engineering, University of Notre Dame, South Bend, IN, USA. His main research interests include control and optimization of cooperative multi-agent systems (focusing on coverage and monitoring applications), networked systems, passivity, symbolic control, machine-learning, robotics, and smart-grid. He is a recipient of several awards, including the 2015 Ceylon Electricity Board Gold Medal, the 2019 President’s Award for Scientific Research in Sri Lanka, and the 2021 Outstanding Ph.D. Dissertation Award in Systems Engineering.
Abstract: Versatile Scalable Robust Optimal Control of Cooperative Multi-Robot Systems in Persistent Monitoring
Cooperative multi-robot systems (CMRS) are increasingly used in many applications such as delivery systems, assembly lines, and persistent monitoring (PM) systems. My research focuses mainly on CMRS in PM that includes applications such as patrolling, surveillance and distributed estimation. Typically, the goal of PM is to optimally monitor a dynamically changing environment using a CMRS.
The existing PM solutions cannot simultaneously incorporate the underlying: multiple objectives, dynamic constraints, inherent uncertainties, and hybrid dynamics of PM systems without sacrificing their scalability and/or optimality. Therefore, this research aims to develop a versatile scalable robust optimal control solution for CMRS in PM.
To this end, a hybrid optimal control framework will be developed combining the strengths of several existing PM solutions and exploiting the latest research discoveries, particularly on control barrier functions, on-line learning and networked systems. The proposed framework will be experimentally validated on real applications of CMRS in PM and beyond.