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 lambda 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.
Supported in part by NRI INSPIRE award 1514882 and by AFRL grant FA865015D1845 (subcontract 669737-1).
Date of this Version
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Spatial point processes, remote sensing, mobile robots, coverage, stopping rule
Date Posted: 21 October 2016
This document has been peer reviewed.