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Stochastically convergent localization of objects and actively controllable sensor-object pose
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1170-7162
2009 (English)In: Proceedings of 10th European Control Conference (ECC 2009), 2009Conference paper, Published paper (Refereed)
Abstract [en]

The problem of object (network) localization using a mobile sensor is examined in this paper. Specifically, we consider a set of stationary objects located in the plane and a single mobile nonholonomic sensor tasked at estimating their relative position from range and bearing measurements. We derive a coordinate transform and a relative sensor-object motion model that leads to a novel problem formulation where the measurements are linear in the object positions. We then apply an extended Kalman filter-like algorithm to the estimation problem. Using stochastic calculus we provide an analysis of the convergence properties of the filter. We then illustrate that it is possible to steer the mobile sensor to achieve a relative sensor-object pose using a continuous control law. This last fact is significant since we circumvent Brockett's theorem and control the relative sensor-source pose using a simple controller.

Place, publisher, year, edition, pages
2009.
Keyword [en]
Autonomous robots, Sensor and mesh networks, Stochastic filtering
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-52835OAI: oai:DiVA.org:kth-52835DiVA: diva2:467878
Conference
10th European Control Conference (ECC 2009), Budapest, 23-26 August 2009
Note
QC 20120110Available from: 2011-12-20 Created: 2011-12-20 Last updated: 2012-01-10Bibliographically approved

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