ICON Lab

Welcome to ICON Lab!

At ICON Lab, we explore the forefront of multi-robot and multi-agent systems, focusing on intelligent, efficient, and autonomous operations in complex environments. Multi-robot systems are increasingly attracting attention because of their wide applications, including search and rescue, surveillance and reconnaissance, smart farming, infrastructure inspection, environmental monitoring, and planetary exploration. Compared to a single robot, a coordinated team of robots can accomplish missions more quickly, robustly, and efficiently while reducing vulnerability to single points of failure.

Our research emphasizes density-driven exploration and task allocation, enabling teams of heterogeneous robots to intelligently and adaptively prioritize regions of interest based on information density, which quantifies the relative importance of each area. By leveraging a comprehensive set of mathematical and computational tools, including optimal transport theory, Wasserstein distance metrics, distributed convex and non-convex optimization, consensus algorithms, and networked control techniques, we rigorously analyze and design multi-agent systems that remain robust to uncertainties such as communication delays, packet drops, and dynamic environmental changes. 

Recently, our lab has expanded into AI-driven robotics, integrating machine learning and artificial intelligence to enhance perception, decision-making, and autonomy in multi-robot systems. With AI, robots can learn from experience, adapt to unknown environments, and collaborate more effectively on complex tasks.

Our primary research areas include:

• Multi-agent and swarm robotics

• Distributed optimization and AI-enhanced autonomous systems

• Uncertainty quantification and decision-making under uncertainty

• Distributed networked control systems and asynchronous algorithms

Explore our work in more detail in the Research and Publication sections on the top menu.