NDLI: A scalable information theoretic approach to distributed robot coordination

Content Provider	IEEE Xplore Digital Library
Author	Julian, Brian J. Angermann, Michael Schwager, Mac Rus, Daniela
Copyright Year	2011
Description	Author affiliation: Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA (Julian, Brian J.; Schwager, Mac; Rus, Daniela) \|\| Institute of Communications and Navigation, DLR Oberpfaffenhofen, 82234 Wessling, Germany (Angermann, Michael)
Abstract	This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots.
Starting Page	5187
Ending Page	5194
File Size	671525
Page Count	8
File Format	PDF
ISBN	9781612844541
ISSN	21530858
e-ISBN	9781612844565
DOI	10.1109/IROS.2011.6095127
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Robot kinematics Approximation methods Joints Mutual information
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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