Fault tolerant collaborative localization for multi-robot system

Multi-robot system is used in some unreachable or dangerous area in order to replace the human operators. In such environments the integrity of localization should be assured by adding a sensor fault diagnosis step. In this paper, we present a method able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the team. The estimator is the informational form of the Kalman Filter (KF) namely Information Filter (IF). The developed residual test is based on the divergence between the predicted and the corrected estimation of the IF, calculated in term of the Kullback-Leibler divergence (KLD). The main contributions of this paper: - developing a method able simultaneously to localize a group of robots and to detect the faulty sensors - using the IF and the KLD as a residual test - Application of the proposed framework to a real environment with real robots.

[1]  Maan El Badaoui El Najjar,et al.  Fault detection and exclusion method for a tightly coupled localization system , 2015, 2015 International Conference on Advanced Robotics (ICAR).

[2]  Maan El Badaoui El Najjar,et al.  Fault tolerant fusion approach based on information theory applied on GNSS localization , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[3]  Christiaan J. J. Paredis,et al.  Fault tolerant localization for teams of distributed robots , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[4]  Hugh Durrant-Whyte,et al.  Introduction to Decentralised Data Fusion , 2006 .

[5]  Fernando Pérez-Cruz,et al.  Kullback-Leibler divergence estimation of continuous distributions , 2008, 2008 IEEE International Symposium on Information Theory.

[6]  Felipe Núñez,et al.  Fault detection and isolation in cooperative mobile robots using multilayer architecture and dynamic observers , 2010, Robotica.

[7]  Li Jiang,et al.  Sensor Fault Detection and Isolation Using System Dynamics Identification Techniques. , 2011 .

[8]  Stergios I. Roumeliotis,et al.  Decentralized multi-robot cooperative localization using covariance intersection , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Janos J. Gertler,et al.  Analytical Redundancy Methods in Fault Detection and Isolation , 1991 .

[10]  Gregory Dudek,et al.  Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  ALLAN DE MEDEIROS MARTINS,et al.  Comparison between Mahalanobis distance and Kullback-Leibler divergence in clustering analysis , 2004 .

[12]  Gaurav S. Sukhatme,et al.  Sensor fault detection and identification in a mobile robot , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[13]  Gaurav S. Sukhatme,et al.  Localization for mobile robot teams using maximum likelihood estimation , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Patric Jensfelt,et al.  Fault detection for mobile robots using redundant positioning systems , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[15]  Philippe Bonnifait.  Localisation precise en position et attitude des robots mobiles d'exterieur a evolutions lentes , 1997 .

[16]  Hugh F. Durrant-Whyte,et al.  A decentralised navigation architecture , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[17]  Stergios I. Roumeliotis,et al.  Distributed multirobot localization , 2002, IEEE Trans. Robotics Autom..

[18]  Michael A. Lexa,et al.  Useful Facts about the Kullback-Leibler Discrimination Distance , 2004 .

[19]  M. Do.  Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models , 2003, IEEE Signal Processing Letters.

[20]  Inderjit S. Dhillon,et al.  Differential Entropic Clustering of Multivariate Gaussians , 2006, NIPS.

[21]  J. Copas,et al.  Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma , 2006 .

[22]  Stergios I. Roumeliotis,et al.  Distributed Maximum A Posteriori Estimation for Multi-robot Cooperative Localization , 2009 .

[23]  Lynne E. Parker,et al.  Sensor Analysis for Fault Detection in Tightly-Coupled Multi-Robot Team Tasks , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[24]  Marco Dorigo,et al.  From Fireflies to Fault-Tolerant Swarms of Robots , 2009, IEEE Transactions on Evolutionary Computation.

[25]  Stefano Panzieri,et al.  Multirobot Localisation Using Interlaced Extended Kalman Filter , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.