Non-parametric inference and coordination for distributed robotics
Name
Rus_Non-parametric inference.pdf
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1.79 MB
Format
Adobe PDF
Checksum (MD5)
cdfdb54011a9c1103401d4c11424eff8
Author(s)
Julian, Brian John
Angermann, Michael
Rus, Daniela L.
Date Issued
December 2012
Journal
Proceedings of the 2012 51st IEEE Conference on Decision and Control (CDC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Julian, Brian J., Michael Angermann, and Daniela Rus. “Non-Parametric Inference and Coordination for Distributed Robotics.” 2012 51st IEEE Conference on Decision and Control (CDC) (December 2012).
Version
Author's final manuscript
Abstract
This paper presents non-parametric methods to infer the state of an environment by distributively controlling robots equipped with sensors. Each robot represents its belief of the environment state with a weighted sample set, which is used to draw likely observations to approximate the gradient of mutual information. The gradient leads to a novel distributed controller that continuously moves the robots to maximize the informativeness of the next joint observation, which is then used to update the weighted sample set via a sequential Bayesian filter. The incorporated non-parametric methods are able to robustly represent the environment state and robots' observations even when they are modeled as continuous-valued random variables having complicated multimodal distributions. In addition, a consensus-based algorithm allows for the distributed approximation of the joint measurement probabilities, where these approximations provably converge to the true probabilities even when the number of robots, the maximum in/out degree, and the network diameter are unknown. The approach is implemented for five quadrotor flying robots deployed over a large outdoor environment, and the results of two separate exploration tasks are discussed.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. School of Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CDC.2012.6427043
