Multi-EKF Localization Algorithm for Multi-Robots Formation Navigation

Article Preview

Abstract:

This research presents a dynamic multi-robots formation navigation algorithm named multi-EKF localization algorithm, which derived from single EKF. In this algorithm, all formation members are treated as landmarks with known association. When the formation is running, the traditional EKF is used for individual robot to get its own localization, and the proposed multi-EKF is used to get the position of the whole formation. By calculating the joint probability distribution, the mean and covariance of the formation position are achieved, which is used to guide and constrain the individual robot localization adjustment. With this method, the multiple mobile robots system shows more stable and robust on formation navigation. The simulation and physical experiment results show the feasibility, efficiency and stability of the proposed algorithm.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 383-390)

Pages:

5648-5655

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Chen Y Q, Wang Z M. Formation navigation: A Review and A New Consideration[C]. IEEE/RSJ Int. Conf. on Volume, 2005: 3181–3186.

Google Scholar

[2] Cowan N, Shakerina O, Vidal R, Sastry S. Vision-based follow-the-leader[C]. Proc. of IEEE/RSJ Intel. Conf. on Intelligent Robots and Systems, 2003: 1796–1801.

DOI: 10.1109/iros.2003.1248904

Google Scholar

[3] Desai J P, Ostrowski J, Kumar V. Controlling formations of multiple mobile robots[C], Proc. of the IEEE Intel. Conf. on Robotics and Automation, 1998: 2864–2869.

DOI: 10.1109/robot.1998.680621

Google Scholar

[4] Ren W, Beard R W, A decentralized scheme for spacecraft formation flying via the virtual structure approach[C]. Proceedings of the 2003 American Control conference, Colorado, 2003: 1746–1751.

DOI: 10.1109/acc.2003.1239847

Google Scholar

[5] Balch T, Arkin R C. Behavior-based formation navigation for multirobot teams[J]. IEEE Transactions on Robotics and Automation, 1999, 14(2): 926-939.

DOI: 10.1109/70.736776

Google Scholar

[6] Schneider F E, Wildermuth D. A potential field based approach to multi robot formation navigation[C]. International Conference on Robotics Intelligent Systems and Signal Processing, Changsha, 2003: 680-685.

DOI: 10.1109/rissp.2003.1285656

Google Scholar

[7] Scokaert P O M, Mayne D Q, Rawlings J B. Suboptimal model predictive control: feasibility implies stability[J]. IEEE Transactions on Automatic Control, 1999, 44(3): 648−654.

DOI: 10.1109/9.751369

Google Scholar

[8] Kanjanawanishkul K. Formation navigation of Omnidirectional Mobile Robots using Distributed Model Predictive Control[C]. Robot Communication and Coordination, 2009: 1–7.

DOI: 10.4108/icst.robocomm2009.5822

Google Scholar

[9] Oubbati M, Palm G. Neural Fields for Controlling Formation of Multiple Robots[C]. Proc. of the 2007 IEEE Intel. Symposium on Computational Intelligence in Robotics and Automation, Jacksonville, 2007: 90-94.

DOI: 10.1109/cira.2007.382841

Google Scholar

[10] Thrun S, Burgard W, Fox D. Probabilistic Robotics[M]. The MIT Press, Cambridge, Massachusetts. (2005).

Google Scholar

[11] Rekleitis I, Dudek G, Milios E. Multi-robot exploration of an unknown environment, efficiently reducing the odometry error. In Proc. of the Intel. Joint Conf. on Artificial Intelligence. 1997: 1340-1346.

Google Scholar

[12] Burgard W, Fox D, Moors M, Simmons R, Thrun S. Collaborative Multi-Robot Exploration[C]. In Proc. Intel. Conf. on Robotics and Automation, San Francisco, 2000: 476-481.

DOI: 10.1109/robot.2000.844100

Google Scholar

[13] Rekleitis I. Cooperative Localization and multi-robot exploration[D]. School of Computer Science McGill University, (2003).

Google Scholar

[14] Schneider F E, Wildermuth D. Using an Extended Kalman Filter for Relative Localisation in a Moving Robot Formation. Fourth International Workshop on Robot Motion and Control, 2004: 85-90.

DOI: 10.1109/romoco.2004.240902

Google Scholar