Abstract
Surveillance is a typical task in the field of multi robot systems that operate as a security system. This paper is concerned with the special problem of observing expanded objects in such settings. A solution in form of a Viterbi based tracking algorithm is presented. Thus a Maximum-a-posteriori (MAP) filtering technique is applied to perform the tracking process. The mathematical background of the algorithm is proposed. The method uses the robot sensors in form of laser range finders and a motion and observation model of the objects being tracked. The special features of the Viterbi based algorithm can be used to support active sensing. The tracking information will facilitate the robots to enhance the perceptual processing via dexterous sensor positioning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yaakov Bar-Shalom and Thomas E. Fortmann, Tracking and Data Association, Academic Press, 1988.
Yaakov Bar-Shalom and Edison Tse, Tracking in a Cluttered Environment With Probabilistic Data Association Automatica, Volume 11, pp. 451–460, Pergamon Press, 1975.
Thomas Clouqueur, Veradej Phipatanasuphorn, Parameswaran Ramanathan and Kewal K. Saluja, Sensor deployment strategy for target detection, MOBICOM 2002: 42–48
A. Elfes, E. Prassler and J. Scholz, Tracking people in a railway station during rush hour, Proc. Computer Vision Systems, First International Conference, ICVS’99, 162–179, Las Palmas, Spain, 13–15 Jan 1999, Springer Verlag.
Ajo Fod, Andrew Howard and Maja J. Mataric, Laser-Based People Tracking, Computer Science Department, University of Southern California, Los Angeles, USA.
G. David Forney, Jr, The Viterbi Algorithm, Proceedings of the IEEE, Volume 61, Number 3, March 1973, 268–278.
Thomas E. Fortmann, Yaakov Bar-Shalom and Molly Scheffe, Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association, IEEE Journal Of Oceanic Engineering, Volume OE-8, Number 3, July 1983.
N. Gordon, D. Salmond and A. Smith, A novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEEE Proceedings F, 140(2), 107–113, 1993.
B. Grocholsky, A. Makarenko and H. Durrant-Whyte, Information-Theoretic Coordinated Control of Multiple Sensor Platforms, IEEE International Conference on Robotics and Automation (ICRA), Taipeh, Taiwan, September 14–19, 2003.
A. Howard, M. Mataric and G. Sukhatme, Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem, 6th International Symposium on Distributed Autonomous Robotics Systems (DARS02), Fukuoka, Japan, June 25–27, 2002.
R. E. Kaiman, A New Approach to Linear Filtering and Prediction Problems, Trans. ASME, J. Basic Engineering, Volume 82, March 1960, 34–45.
P. C. Mahalanobis, On the Generalized Distance in Statistics, Proc. Natl. Inst. Science, Volume 12, Calcutta, 1936, 49–55.
Peter S. Maybeck, Stochastic Models, Estimation, and Control. Academic Press, New York, San Francisco, London, 1979.
M. Pitt and N. Shephard, Filtering via simulation: auxiliary particle filters, Journal of the American Statistical Association, 994(446), 1999.
T. Quach and M. Farrooq, Maximum Likelihood Track Formation with the Viterbi Algorithm, Proceedings of the 33rd Conference on Decision and Control, Lake Buena Vista, FL, December 1994.
F. E. Schneider and D. Wildermuth, A potential field based approach to multi robot formation navigation, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing (RISSP), Changsha, Hunan, China, October 8–13, 2003.
F. E. Schneider and D. Wildermuth, Motion co-ordination for formations of multiple mobile robots, Conference on Field and Servive Robotics, Helsinki, Finnland, June 11–13, 2001.
Dirk Schulz, Wolfram Burgard, Dieter Fox and Armin B. Cremers, Tracking Multiple Moving Objects with a Mobile Robot, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, 2001.
S. Sclaroff and R. Rosales, Improved tracking of multiple humans with trajectory prediction and occlusion modeling, IEEE Conference on Computer Vision and Pattern Recognition, Workshop on the interpretation of Visual Motion, Santa Barbara, CA, 1998.
Robert H. Shumway and David S. Stoffer, Time Series Analysis and Its Applications, Springer, 2000.
Sebastian Thrun, Learning metric-topological maps for indoor mobile robot navigation, Artificial Intelligence, 1, 21–71, 1999.
Sebastian Thrun, Dieter Fox and Wolfram Burgard, Markov localization for mobile robots in dynamic environments, Artificial Intelligence, 11, 391–427, 1999.
Don J. Torrieri, Statistical Theory of Passive Location Systems, IEEE Transactions on Aerospace and Electronic Systems, Volume AES-20, Number 2, March 1984.
Andrew J. Viterbi, Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm, IEEE Transactions On Information Theory, Volume IT-13, Number 2, April 1967.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer
About this paper
Cite this paper
Schneider, F.E., Kräußling, A., Wildermuth, D. (2007). An approach to active sensing using the Viterbi algorithm. In: Alami, R., Chatila, R., Asama, H. (eds) Distributed Autonomous Robotic Systems 6. Springer, Tokyo. https://doi.org/10.1007/978-4-431-35873-2_11
Download citation
DOI: https://doi.org/10.1007/978-4-431-35873-2_11
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-35869-5
Online ISBN: 978-4-431-35873-2
eBook Packages: EngineeringEngineering (R0)