Consider domains such as agronomy and environmental science in which access to high-resolution spat-temporal data is critical in understanding the physical world. One of the main bottlenecks in such settings is collecting the data in the first place. In this talk, I will present an overview of my group's research on planning and coordination algorithms for data collection with heterogeneous robot teams.
I will focus on three types of algorithmic problems that are grounded in environmental applications but address fundamental challenges in realizing multi-robot systems. I will start with the question of where should the robots gather data from to learn a spatiotemporal field. Then, I will discuss how heterogeneity in a multi-robot system can be exploited to enable long-term autonomy. Finally, I will discuss our recent efforts aimed at bridging the gap between theory and practice by studying issues of resilience and risk-aware planning in the presence of uncertainty and adversarial attacks/failures.
Pratap Tokekar is an Assistant Professor in the Department of Computer Science and UMIACS at the University of Maryland. Between 2015 and 2019, he was an Assistant Professor at the Department of Electrical and Computer Engineering at Virginia Tech. Previously, he was a Postdoctoral Researcher at the GRASP lab of University of Pennsylvania. He obtained his Ph.D. in Computer Science from the University of Minnesota in 2014 and Bachelor of Technology degree in Electronics and Telecommunication from College of Engineering Pune, India in 2008. He is a recipient of the NSF CAREER award (2020) and CISE Research Initiation Initiative award (2016). He serves as an Associate Editor for the IEEE Transactions on Robotics, IEEE Transactions of Automation Science & Engineering, and the ICRA and IROS Conference Editorial Board.