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Mobile robots as remote sensors for spatial point process models

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct, 2016.

Paul Reverdy and D. E. Koditschek
Electrical and Systems Engineering, University of Pennsylvania

Full PDF (preprint) | Penn Scholarly Commons

Fig. 1. Example trajectory.

Abstract
       Spatial point process models are a commonly-used statistical tool for studying the distribution of objects of interest in a domain. We study the problem of deploying mobile robots as remote sensors to estimate the parameters of such a model, in particular the intensity parameter λ which measures the mean density of points in a Poisson point process. This problem requires covering an appropriately large section of the domain while avoiding the objects, which we treat as obstacles. We develop a control law that covers an expanding section of the domain and an online criterion for determining when to stop sampling, i.e., when the covered area is large enough to achieve a desired level of estimation accuracy, and illustrate the resulting system with numerical simulations.
Acknowledgements This work was funded in part by the Air Force Office of Scientific Research under the MURI FA9550–10–1−0567, by NRI INSPIRE award 1514882 and by AFRL grant FA865015D1845 (subcontract 669737–1).
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