Multi-robot systems are already playing a crucial role in various domains such as manufacturing and warehouse automation. In the future, these systems will be incorporated into our daily lives through drone-delivery services and smart-mobility systems that comprise thousands of autonomous vehicles, to give a few examples. The anticipated benefits of multi-robot systems are numerous, ranging from increased safety and efficiency, to broader societal facets such as sustainability. However, to reap those rewards we must develop algorithms that can adapt rapidly to unexpected changes on a massive scale. Importantly, these algorithms must capture (i) dynamical and collision-avoidance constraints of individual robots, (ii) interactions between multiple robots, and (iii), more broadly, the interaction of those systems with their environment. These considerations give rise to extremely complex and high-dimensional optimization problems that need to be solved in real-time.
In this talk, I will present progress on the design of systematic control and decision-making mechanisms to allow the effective, and societally-equitable deployment of multi-robot systems. I will highlight results on fundamental capabilities for multi-robot systems (e.g., motion planning and task allocation), as well as applications in smart-mobility systems. I will also discuss challenges and opportunities for smart mobility in addressing societal issues, including traffic congestion and fairness.
BIO: Kiril Solovey is a roboticist specializing in multi-robot systems and their applications to smart mobility. He is currently a Postdoctoral Scholar in the Faculty of Computer Science at the Technion. Prior to that, he was a postdoc in the Department of Aeronautics and Astronautics, Stanford University. He obtained a PhD in Computer Science from Tel Aviv University, where he was advised by Dan Halperin. Kiril's research focuses on the design of scalable algorithmic approaches for multi-robot systems. His work draws upon ideas across the disciplines of computer science, engineering, and transportation science, to develop scalable methods with substantial guarantees. For his academic work, he received multiple awards, including the Clore Scholars and Fulbright Postdoctoral Fellowships, best paper awards and nominations (at Robotics: Science and Systems, International Conference on Robotics and Automation, International Symposium on Multi-Robot and Multi-Agent System, and European Control Conference), and teaching awards.