Abstract
We propose a new approach to the simultaneous cooperative localization of a group of robots capable of sensing their own motion on the plane and the relative position of nearby robots. In the last decade, the use of distributed optimal Kalman filters (KF) to solve this problem have been studied extensively. In this paper, we propose to use a sub-optimal Kalman filter (denoted by EA). EA requires significantly less computation and communication resources then KF. Furthermore, in some cases, EA provides better localization.
In this paper EA is analyzed in a soft “thermodynamic” fashion i.e. relaxing assumptions are used during the analysis. The goal is not to derive hard lower or upper bounds but rather to characterize the robots expected behavior. In particular, to predict the expected localization error. The predictions were validated using simulations. We believe that this kind of analysis can be beneficial in many other cases.
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
Borenstein, J., Everett, H.R., Feng, L.: Navigating Mobile Robots: Systems and Techniques. A. K. Peters, Ltd., Natick (1996)
Elor, Y., Bruckstein, A.M.: A thermodynamic approach to the analysis of multi-robot cooperative localization under independent errors. Tech. rep., Technion (Mar 2010) (under revision for ANTS 2010)
Fox, D., Burgard, W., Kruppa, H., Thrun, S.: A probabilistic approach to collaborative multi-robot localization. Autonomous Robots 8(3), 325–344 (2000)
Kurazume, R., Nagata, S., Hirose, S.: Cooperative positioning with multiple robots. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, vol. 2 (1994)
Martinelli, A.: Improving the precision on multi robot localization by using a series of filters hierarchically distributed. In: Proc. IEEE/RSJ Int. Conf. on Intel. Robots and Systems, San Diego, CA, USA (October 2007)
Mourikis, A., Roumeliotis, S.: Optimal sensing strategies for mobile robot formations: Resource-constrained localization. In: Robotics: Science and Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA (June 2005)
Mourikis, A., Roumeliotis, S.: Performance analysis of multirobot cooperative localization. IEEE Trans. on Robotics 22(4), 666–681 (2006)
Rekleitis, I., Dudek, G., Milios, E.: Multi-robot collaboration for robust exploration. Annals of Math and Artificial Intel. 31(1), 7–40 (2001)
Roumeliotis, S., Bekey, G.: Distributed multirobot localization. IEEE Trans. on Robotics and Automation 18(5), 781–795 (2002)
Roumeliotis, S.I., Rekleitis, I.M.: Propagation of uncertainty in cooperative multirobot localization: Analysis and experimental results. Auton. Robots 17(1) (2004)
Sanderson., A.C.: A distributed algorithm for cooperative navigation among multiple mobile robots. Advanced Robotics 12, 335–349 (1997)
Thrun, S.: Robotic mapping: a survey. In: Exploring Artificial Intel. in the New Millenium, pp. 1–35. Morgan Kaufmann Publishers Inc., San Francisco (2003)
Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust monte carlo localization for mobile robots. Artificial Intelligence 128(1-2), 99–141 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Elor, Y., Bruckstein, A.M. (2010). A Thermodynamic Approach to the Analysis of Multi-robot Cooperative Localization under Independent Errors. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-15461-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15460-7
Online ISBN: 978-3-642-15461-4
eBook Packages: Computer ScienceComputer Science (R0)