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Paper FrC13.4

Parker, Lynne (University of Tennessee), Reardon, Christopher M. (University of Tennessee), Choxi, Heeten (Lockheed Martin Advanced Technology Laboratories), Bolden, Cortney (Lockheed Martin Advanced Technology Laboratories)

Using Critical Junctures and Environmentally-Dependent Information for Management of Tightly-Coupled Cooperation in Heterogeneous Robot Teams

Scheduled for presentation during the Regular Sessions "Distributed Robot Systems - III" (FrC13), Friday, May 15, 2009, 14:30−14:50, Room: 505

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Distributed Robot Systems, Networked Robots

Abstract

This paper addresses the challenge of forming appropriate heterogeneous robot teams to solve "tightly-coupled, potentially multi-robot" tasks, in which the robot capabilities may vary over the environment in which the task is being performed. Rather than making use of a permanent tightly-coupled robot team for performing the task, our approach aims to recognize when tight coupling is needed, and then only form tight cooperative teams at those times. This results in important cost savings, since coordination is only used when the independent operation of the team members would put mission success at risk. Our approach is to define a new semantic information type, called "environmentally dependent information", which allows us to capture certain environmentally-dependent perceptual constraints on vehicle capabilities. We define locations at which the robot team must transition between tight and weak cooperation as "critical junctures". Note that these critical juncture points are a function of the robot team capabilities and the environmental characteristics, and are not due to a change in the task itself. We calculate critical juncture points by making use of our prior ASyMTRe approach, which can automatically configure heterogeneous robot team solutions to enable sharing of sensory capabilities across robots. We demonstrate these concepts in experiments involving a human-controlled blimp and an autonomous ground robot in a target localization task.

 

 

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