*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Interactions in Multi-Robot Systems
Committee:
Dr. Magnus Egerstedt, ECE, Chair , Advisor
Dr. Yorai Wardi, ECE
Dr. Anthony Yezzi, ECE
Dr. Aaron Ames, ECE
Dr. Haomin Zhou, Math
Abstract:
The objective of this research is to develop a framework for multi-robot coordination and control with emphasis on human-swarm and inter-agent interactions. We focus on two problems: in the first we address how to enable a single human operator to externally influence an arbitrarily-sized team of robots. By directly imposing density functions on the environment, the user is able to abstract away the size of the swarm and focus on the task, e.g., to achieve specified geometric configurations, or to maneuver the swarm as a whole. In order to pursue this approach, contributions were made to the problem of coverage of time-varying density functions. In the second problem, we address the characterization of inter-agent interactions and enforcement of desired interaction patterns in a provably safe (i.e., collision free) manner, e.g., for achieving rich motion patterns in a shared space, or for mixing of sensor information. We use elements of the braid group, which allows us to symbolically characterize classes of interaction patterns. We further construct a new specification language that allows us to provide rich, temporally-layered specifications to the multi-robot mixing framework, and present algorithms that significantly reduce the search space of specification-satisfying symbols with exactness guarantees. We also synthesize provably safe controllers that generate trajectories to satisfy these symbolic inputs.