Research teams
George Mason University
GMU team aims to build robotic agents that can robustly operate in dynamic and contested environments using sensor-actuator pairs that are distributed in the system. We address two weaknesses that conventional robot design approach has: the centralized architecture reliant on CPU that causes brittleness, and the top-down approach based on idealized mathematical models that forces us to pursue precision. We explore the viability of a bottom-up approach in building intelligence into a robot or a group of robots using individually weak components that interact with each other. We will specifically study how those components can utilize analog interaction for achieving faster response time and better tolerance to imprecision. Through the case-studies using Lighter-Than-Air (LTA) vehicles, we seek to characterize the robustness of such systems and construct a generalizable theory for their analysis.
University of Florida
Mobile autonomy holds great promise for applications ranging from precision agriculture and self-driving cars to assistive healthcare and warehouse automation. These applications and many others all require agents to gather information using onboard sensors, process it with onboard computers, share it using only onboard communications hardware, and then make control decisions. Some approaches to autonomy implicitly or explicitly require external resources, such as a lab computer to execute demanding computations. To enable autonomy beyond controlled laboratory settings, fundamentally new developments are required to let agents execute all tasks themselves. One way of doing so is through control-aware computation and computation-aware control. Agents have limited onboard computational power, and this must be accounted for in two ways. First, agents' controllers can account for the fact that they are driven by imperfect information and the fact that such information can enter feedback loops. Second, agents' computations can be modified to account for the impact that their imperfections will have upon agents' controllers. Mobile autonomy presents fundamental challenges because it tightly couples computation and control, though this coupling can be accounted for and even exploited to enable greater degrees of autonomy.Â
Lehigh University
Inspired by ant swarms that use physical interaction to survive in wild environments, our team develops novel swarm behaviors that allow robots to operate in complex environments relying on physical inter-robot interaction. We co-design robot hardware and control algorithms to allow small agile robots to cooperate and create collective behaviors. The physical interconnection between aerial robots brings new theoretical challenges in the design and control of aerial vehicles. We will design distributed control algorithms in this type of vehicle that requires dealing with a highly complex configuration space due to a large number of configurations from the inter-connected swarm.