Decentralized Estimation and Control of Graph Connectivity in Mobile Sensor Networks

Peng Yang, Randy A. Freeman, Geoffrey Gordon, Kevin Lynch, Siddhartha Srinivasa, and Rahul Sukthankar
American Control Conference, June, 2008.


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Abstract
The ability of a robot team to reconfigure itself is useful in many applications: for metamorphic robots to change shape, for swarm motion towards a goal, for biological systems to avoid predators, or for mobile buoys to clean up oil spills. In many situations, auxiliary constraints, such as connectivity between team members and limits on the maximum hop-count, must be satisfied during reconfiguration. In this paper, we show that both the estimation and control of the graph connectivity can be accomplished in a decentralized manner. We describe a decentralized estimation procedure that allows each agent to track the algebraic connectivity of a time-varying graph. Based on this estimator, we further propose a decentralized gradient controller for each agent to maintain global connectivity during motion.

Keywords
decentralized multi-agent control, connectivty estimation, mobile sensor networks

Notes
Associated Center(s) / Consortia: Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Manipulation Lab
Note: Invited Session on "Cooperative Control and Sensing Algorithms for Mobile Sensor Networks: Theory and Practice"

Text Reference
Peng Yang, Randy A. Freeman, Geoffrey Gordon, Kevin Lynch, Siddhartha Srinivasa, and Rahul Sukthankar, "Decentralized Estimation and Control of Graph Connectivity in Mobile Sensor Networks," American Control Conference, June, 2008.

BibTeX Reference
@inproceedings{Gordon_2008_6082,
   author = "Peng Yang and Randy A. Freeman and Geoffrey Gordon and Kevin Lynch and Siddhartha Srinivasa and Rahul Sukthankar",
   title = "Decentralized Estimation and Control of Graph Connectivity in Mobile Sensor Networks",
   booktitle = "American Control Conference",
   month = "June",
   year = "2008",
   Notes = "Invited Session on "Cooperative Control and Sensing Algorithms for Mobile Sensor Networks: Theory and Practice""
}