Mobile robots as remote sensors for spatial point process models

Paul Reverdy; Daniel E. Koditschek

doi:10.1109/IROS.2016.7759441

Mobile robots as remote sensors for spatial point process models

Paul Reverdy, Daniel E. Koditschek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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.

Original language	English (US)
Title of host publication	IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2847-2852
Number of pages	6
ISBN (Electronic)	9781509037629
DOIs	https://doi.org/10.1109/IROS.2016.7759441
State	Published - Nov 28 2016
Externally published	Yes
Event	2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of Duration: Oct 9 2016 → Oct 14 2016

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
Volume	2016-November
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Country/Territory	Korea, Republic of
City	Daejeon
Period	10/9/16 → 10/14/16

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/IROS.2016.7759441

Cite this

Reverdy, P., & Koditschek, D. E. (2016). Mobile robots as remote sensors for spatial point process models. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2847-2852). Article 7759441 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2016.7759441

Mobile robots as remote sensors for spatial point process models. / Reverdy, Paul; Koditschek, Daniel E.
IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2847-2852 7759441 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Reverdy, P & Koditschek, DE 2016, Mobile robots as remote sensors for spatial point process models. in IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems., 7759441, IEEE International Conference on Intelligent Robots and Systems, vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 2847-2852, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, Korea, Republic of, 10/9/16. https://doi.org/10.1109/IROS.2016.7759441

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AB - 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.

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Mobile robots as remote sensors for spatial point process models

Abstract

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