In the last few years, simultaneous localization and mapping became an important topic of research in the robotics community. This article proposes an approach for autonomous navigation of mobile robots in faulty situations. The main objective is to extend the fault tolerance strategy to simultaneous localization and mapping in presence of sensor faults or software faults in the data fusion process. Fault detection and isolation technique is performed based on duplication–comparison method and structured residuals. The proposed fault tolerance approach is based on the extended Kalman filter for simultaneous localization and mapping when an absolute localization sensor is available. The validation of the proposed approach and the extended Kalman filter for simultaneous localization and mapping algorithm is performed from experiments employing an omnidrive mobile robot, equipped with embedded sensors, namely: wheel encoders, gyroscope, two laser rangefinders and external sensor for the absolute position (indoor global positioning system). The obtained results demonstrate the effectiveness of the proposed approach where it was found that its fault tolerance performance is based essentially on the selected residuals and the values of the fault detection thresholds to be used for the fault detection and isolation.
MotarlierPChatilaR. Stochastic multisensory data fusion for mobile robot location and environment modeling. In: Proceedings of the 5th international symposium on robotics research, Tokyo, Japan, 28–31 August 1989, pp.85–94. Cambridge, MA: MIT Press.
3.
Durrant-WhyteHF.Uncertain geometry in robotics. IEEE J Robot Autom1988; 4(1): 23–31.
4.
OhSHahnMKimJ.Dynamic EKF-based SLAM for autonomous mobile convergence platforms. Multimed Tool Appl2015; 74(16): 6413–6430.
5.
FethiDNemraALouadjK, et al. Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control. Adv Mech Eng2018; 10(1): 1687814017736653.
6.
SchleicherDBergasaLMOcañaM, et al. Real-time hierarchical outdoor SLAM based on stereovision and GPS fusion. IEEE Trans Intell Transp Syst2009; 10(3): 440–452.
7.
ChoiJLeeYKimT, et al. EKF SLAM using acoustic sources for autonomous underwater vehicle equipped with two hydrophones. In: Proceedings of the OCEANS 2016 MTS/IEEE Monterey, Monterey, CA, 19–23 September 2016, pp.1–4. New York: IEEE.
8.
ValienteDGilAReinosoÓ, et al. Improved omnidirectional odometry for a view-based mapping approach. Sensors2017; 17(2): 325.
9.
ValienteDPayáLJiménezL, et al. Visual information fusion through Bayesian inference for adaptive probability-oriented feature matching. Sensors2018; 18(7): 2041.
10.
LoureiroRMerzoukiRBouamamaBO.Bond graph model based on structural diagnosability and recoverability analysis: application to intelligent autonomous vehicles. IEEE Trans Veh Technol2012; 61(3): 986–997.
11.
LoureiroRBenmoussaSTouatiY, et al. Integration of fault diagnosis and fault-tolerant control for health monitoring of a class of MIMO intelligent autonomous vehicles. IEEE Trans Veh Technol2014; 63(1): 30–39.
12.
TouatiYMerzoukiRBouamamaBO.Robust diagnosis to measurement uncertainties using bond graph approach: application to intelligent autonomous vehicle. Mechatronics2012; 22(8): 1148–1160.
13.
BenmoussaSBouamamaBOMerzoukiR.Bond graph approach for plant fault detection and isolation: application to intelligent autonomous vehicle. IEEE Trans Autom Sci Eng2014; 11(2): 585–593.
14.
KamelMAYuXZhangY.Fault-tolerant cooperative control design of multiple wheeled mobile robots. IEEE Trans Control Syst Technol2018; 26(2): 756–764.
15.
AvramRCZhangXMuseJ.Nonlinear adaptive fault-tolerant quadrotor altitude and attitude tracking with multiple actuator faults. IEEE Trans Control Syst Technol2018; 26(2): 701–707.
16.
YangKYuhJChoiSK.Fault-tolerant system design of an autonomous underwater vehicle ODIN: an experimental study. Int J Syst Sci1999; 30(9): 1011–1019.
17.
XueWGuoYQZhangXD. A bank of Kalman filters and a robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator. In: Proceedings of the 2nd international conference on innovative computing, information and control (ICICIC 2007), Kumamoto, Japan, 5–7 September 2007, p.10. New York: IEEE.
18.
Escamilla-AmbrosioPMortN.A hybrid Kalman filter-fuzzy logic multisensor data fusion architecture with fault tolerant characteristics. In: Proceedings of the international conference on artificial intelligence, 2001, pp.361–367, http://www.cic.ipn.mx/∼pescamilla/papers/385AI.pdf
19.
CaspiPSalemR.Threshold and bounded-delay voting in critical control systems. In: Proceedings of the international symposium on formal techniques in real-time and fault-tolerant systems, Pune, India, 20–22 September 2000, pp.70–81. Berlin: Springer-Verlag.
20.
IbarguengoytiaPHSucarLEVaderaS.Real time intelligent sensor validation. IEEE Trans Power Syst2001; 16(4): 770–775.
AbidAKhanMTde SilvaC.Fault detection in mobile robots using sensor fusion. In: Proceedings of the 10th international conference on computer science & education (ICCSE), Cambridge, 22–24 July 2015, pp.8–13. New York: IEEE.
23.
AbidAKhanMT.Multi-sensor, multi-level data fusion and behavioral analysis based fault detection and isolation in mobile robots. In: Proceedings of the 8th annual information technology, electronics and mobile communication conference (IEMCON), Vancouver, BC, Canada, 3–5 October 2017, pp.40–45. New York: IEEE.
24.
TanLDuanZ. Particle filter based robust simultaneous localization and map building for mobile robots. In: Proceedings of the international conference on automation and logistics, Qingdao, China, 1–3 September 2008, pp.2512–2515. New York: IEEE.
25.
Al HageJEl NajjarMEPomorskiD. Multi-sensor fusion approach with fault detection and exclusion based on the Kullback–Leibler Divergence: application on collaborative multi-robot system. Inform Fusion2017; 37: 61–76.
26.
Sadeghzadeh-NokhodberizNPoshtanJ.Distributed interacting multiple filters for fault diagnosis of navigation sensors in a robotic system. IEEE Trans Syst Man Cybern Syst2017; 47(7): 1383–1393.
27.
GuoDZhongMZhouD.Multisensor data-fusion-based approach to airspeed measurement fault detection for unmanned aerial vehicles. IEEE Trans Instrum Meas2018; 67(2): 317–327.
28.
AkaiNMoralesLYMuraseH.Simultaneous pose and reliability estimation using convolutional neural network and Rao–Blackwellized particle filter. Adv Robot2018; 32(17): 930–944.
29.
NobiliSTinchevGFallonM.Predicting alignment risk to prevent localization failure. In: Proceedings of the international conference on robotics and automation (ICRA), Brisbane, QLD, Australia, 21–25 May 2018, pp.1003–1010. New York: IEEE.
30.
AlmqvistHMagnussonMKucnerTP, et al. Learning to detect misaligned point clouds. J Field Robot2018; 35(5): 662–677.
31.
BaderKLussierBSchönW. Functional diversification for software fault tolerance in data fusion: a real application on Kalman filters for mobile robot yaw estimation. In: Proceedings of the annual European safety and reliability conference (ESREL 2015), Zürich, 7–10 September 2015, pp.3977–3985. The Netherlands: CRC Press/Balkema.
32.
BaderKLussierBSchönW.A fault tolerant architecture for data fusion: a real application of Kalman filters for mobile robot localization. Robot Auton Syst2017; 88: 11–23.
33.
Festo Didactic GmbH. Robotino: mobile robot platform for research and training. Denkendorf: Festo Didactic GmbH, 2013.
34.
PattonRJ.Robust model-based fault diagnosis: the state of the art. IFAC Proc Vol1994; 27(5): 1–24.
