Arizona State University’s Spring Berman presents “A Control and Estimation Framework for Adaptive Robotic Swarms.” The event will be held in the Van Leer 218 from 1-2 p.m. and is open to the public.

Abstract

In recent years, there has been an increasing focus on the development of robotic swarms that can perform tasks over large spatial and temporal scales. We are addressing the problem of reliably controlling swarms in realistic scenarios where the robots lack global position information, communication, and prior data about the environment. As in natural swarms, the highly resource-constrained platforms would be restricted to local information about swarm members and features that they randomly encounter in the course of exploration.

We are developing a rigorous control and estimation framework for swarms that are subject to these constraints and are deployed in dynamic, unstructured environments. This framework will enable swarms to operate largely autonomously, with user input consisting only of high-level directives that map to a small set of robot parameters. We use stochastic and deterministic models from chemical kinetics and fluid dynamics to describe the robots’ roles, task transitions, spatiotemporal distributions, and manipulation dynamics at both the microscopic (individual) and macroscopic (population) levels. In this talk, I will describe our work on various aspects of the framework, including strategies for mapping, task allocation, boundary coverage, formation control, herding, and ant-inspired collective transport. To validate these techniques, we are building a swarm of small manipulator-equipped robots, called “Pheeno,” designed to be low-cost, customizable platforms for multi-robot research and robotics education.

Bio

Spring M. Berman is an assistant professor of Mechanical and Aerospace Engineering and graduate faculty in Computer Science and Exploration Systems Design at Arizona State University. She received the B.S.E. degree in Mechanical and Aerospace Engineering from Princeton University in 2005, and the Ph.D. degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 2010. From 2010 to 2012, Berman was a postdoctoral researcher in Computer Science at Harvard University. Her research focuses on controlling swarms of resource-limited robots with stochastic behaviors to reliably perform collective tasks in realistic environments. She is also interested in the analysis of collective behaviors in biology and biologically inspired control of distributed systems. She was a recipient of the 2014 DARPA Young Faculty Award.