TY - GEN
T1 - Communication efficient decentralized Gaussian Process Fusion for multi-UAS path planning
AU - Allamraju, Rakshit
AU - Chowdhary, Girish
N1 - Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - We present a decentralized, communication efficient method for joint sensing and learning (regression) of spatiotemporal phenomena using a team of autonomous networked sensing agents. Gaussian Process (GP) priors are utilized in a Bayesian regression framework over the unknown function, with our main contribution being the introduction of a communication efficient decentralized GP inference algorithm and associated agent path planning strategy. Our method relies on reducing communication between agents by exchanging GP model parameters, instead of actual measured data. The global GP model is leveraged to maximize exploration of the sensing space using a policy iteration planning framework. The performance of the presented method is compared against state-of-the-art consensus based and GP-DDF method, and it is shown that the presented method provides significant improvements in regression accuracy while reducing communication required.
AB - We present a decentralized, communication efficient method for joint sensing and learning (regression) of spatiotemporal phenomena using a team of autonomous networked sensing agents. Gaussian Process (GP) priors are utilized in a Bayesian regression framework over the unknown function, with our main contribution being the introduction of a communication efficient decentralized GP inference algorithm and associated agent path planning strategy. Our method relies on reducing communication between agents by exchanging GP model parameters, instead of actual measured data. The global GP model is leveraged to maximize exploration of the sensing space using a policy iteration planning framework. The performance of the presented method is compared against state-of-the-art consensus based and GP-DDF method, and it is shown that the presented method provides significant improvements in regression accuracy while reducing communication required.
UR - http://www.scopus.com/inward/record.url?scp=85027079026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027079026&partnerID=8YFLogxK
U2 - 10.23919/ACC.2017.7963639
DO - 10.23919/ACC.2017.7963639
M3 - Conference contribution
AN - SCOPUS:85027079026
T3 - Proceedings of the American Control Conference
SP - 4442
EP - 4447
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -