Improving object tracking through distributed exploration of an information map

Izaak D. Neveln, Lauren M. Miller, Malcolm A. Maciver, Todd D. Murphey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Tracking the position of moving objects requires tight coordination of sensing and movement, in both biological contexts such as prey pursuit and capture, and in target localization by mobile robots. Algorithms for target tracking often use a probabilistic map, or information map, of the domain to guide active search. Though it is reasonable to expect that the best approach would be to choose control actions driving the robot toward the maximum of this information map, we show improved performance in simulation by using a simple heuristic incorporating the time history of robot movement into the map. Furthermore, our results indicate that as the distribution of robot positions approaches the distribution of the density of information, the variance of the estimate is decreased and tracking improves. We conclude that control actions based solely on information maximization may under-perform in information orientated tasks, such as the estimation of moving target positions.

Original languageEnglish (US)
Title of host publicationIROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3441-3447
Number of pages7
ISBN (Electronic)9781479969340
DOIs
StatePublished - Oct 31 2014
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
CountryUnited States
CityChicago
Period9/14/149/18/14

ASJC Scopus subject areas

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

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    Neveln, I. D., Miller, L. M., Maciver, M. A., & Murphey, T. D. (2014). Improving object tracking through distributed exploration of an information map. In IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3441-3447). [6943042] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2014.6943042