Thesis defense: Adaptive communication networks for heterogeneous teams of robots

Speaker: Stephanie Gil , CSAIL

Date: Wednesday, December 04, 2013

Time: 2:00 PM to 3:00 PM Note: all times are in the Eastern Time Zone

Refreshments: 1:45 PM

Public: Yes

Location: 32-D463 (Star)

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Host: Daniela Rus

Contact: Mieke Moran, 617-253-5817,

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Reminder Subject: TALK: Thesis defense: Adaptive communication networks for heterogeneous teams of robots

Seemingly, the era of ubiquitous connectivity has arrived, with smart phones, tablets, and small computing devices bringing internet straight to our fingertips -- or has it? Two thirds of the world still does not have access to the internet, and a lack of realistic communication guarantees for multi-agent robotic networks are standing in the way of taking these systems from research labs into the real world. In this thesis we consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots, while allowing clients maximum freedom to achieve their primary coordination task. We develop algorithms and performance guarantees for maintaining a desired communication quality over the entire heterogeneous team of controlled mobile routers and non-cooperative clients.

We focus on the router placement problem where we explicitly allow for client motion over a priori unknown trajectories. We are able to scale up to handle large sets of clients by deriving algorithms that compute a small representative set of clients that can be updated quickly as clients move along their trajectories. Our router placement algorithm applied to this sparse set provides a bounded error approximate solution. We show that we can incorporate real-time wireless channel feedback into our controllers and demonstrate the capabilities of our adaptive communication network in hardware experiments in dynamic environments and where neither client positions nor an obstacle map is known. Finally, we derive distributed controllers for the special case where clients are static. We support our theoretical claims with experimental results using AscTec hummingbird platforms as well as iRobot Create platforms of small ~10 client and large ~500 virtual client implementations.

Thesis committee: Daniela Rus (thesis supervisor), Nick Roy, Emilio Frazzoli

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Created by Mieke Moran Email at Tuesday, December 03, 2013 at 8:37 AM.