@import url(http://kodlab.seas.upenn.edu/pub/skins/sinorca/basic.css); @import url(http://kodlab.seas.upenn.edu/pub/skins/sinorca/layout.css); @import url(http://kodlab.seas.upenn.edu/pub/skins/sinorca/sinorca.css);
Fig. 1. Example trajectory.
|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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