Abnormal Movement State Detection and Identification for Mobile Robots Based on Neural Networks

  • Zhuohua Duan
  • Zixing Cai
  • Xiaobing Zou
  • Jinxia Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


Movement state estimation plays an important role in navigating and movement controlling for wheeled mobile robots (WMRs), especially those in unknown environments such as planetary exploration. When exploring in unknown environments, mobile robot suffers from many kinds of abnormal movement state, such as baffled by an obstacle, slipping, among others. This paper employs neural network method to detect abnormal movement states. Specifically, it exploits the kinematics of the normal and abnormal movement states of the monitored robot. Several residuals are exploited and four probabilistic neural networks are used to classify the residuals. Simulation experiments show that the methods can detect and identify most abnormal movement states.


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  1. 1.
    Roumeliotis, S.I., Sukhatme, G.S., Bekey, G.A.: Sensor Fault Detection and Identification in a Mobile Robot. In: IEEE/RSJ Int.l Conf. on Intelligent Robots and Systems, pp. 1383–1388 (1998)Google Scholar
  2. 2.
    Goel, P., Dedeoglu, G., Roumeliotis, S.I., Sukhatme, G.S.: Fault Detection and Identification in a Mobile Robot Using Multiple Model Estimation And Neural Network. In: IEEE Int.l Conf. on Robotics & Automation, pp. 2302–2309 (2000)Google Scholar
  3. 3.
    Washington, R.: On-board Real-time State and Fault Identification for Rovers. In: IEEE Int.l Conf. on Robotics & Automation, pp. 1175–1181 (2000)Google Scholar
  4. 4.
    Verma, V., Gordon, G., Simmons, R.: Efficient Monitoring for Planetary Rovers. In: International Symposium on Artificial Intelligence and Robotics in Space (2003)Google Scholar
  5. 5.
    Kawabata, K., Okina, S., Fujii, T., Asama, H.: A System for Self-diagnosis of an Autonomous Mobile Robot Using an Internal State Sensory System: Fault Detection And Coping with The Internal Condition. Advanced Robotics 9, 925–950 (2003)CrossRefGoogle Scholar
  6. 6.
    Dixon, W.E., Walker, I.D., Dawson, D.M.: Fault Detection for Wheeled Mobile Robots with Parametric Uncertainty. In: IEEE/ASME Int.l Conf. on Advanced Intelligent Mechatronics, pp. 1245–1250 (2001)Google Scholar
  7. 7.
    Madani, K.: A Survey of Artificial Neural Networks Based Fault Detection and Fault Diagnosis Techniques. In: Proceedings of International Joint Conference on Neural Networks, Washington, DC, USA, pp. 3442–3446 (1999)Google Scholar
  8. 8.
    West, B.P., May, S.R., Eastwood, J.E., Rossen, C.: Interactive Seismic Facies Classification Using Textural Attributes and Neural Networks. Leading Edge (Tulsa, OK) 10, 1042–1049 (2002)CrossRefGoogle Scholar
  9. 9.
    Cai, Z.X., Zou, X.B., Wang, L., Duan, Z.H., Yu, J.X.: A Research on Mobile Robot Navigation Control in Unknown Environment: Objectives, Design and Experiences. In: Proceedings of Korea-Sino Symposium on Intelligent Systems, Busan, Korea, pp. 57–63 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhuohua Duan
    • 1
    • 2
  • Zixing Cai
    • 1
  • Xiaobing Zou
    • 1
  • Jinxia Yu
    • 1
    • 3
  1. 1.College of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Department of Computer ScienceShaoguan UniversityShaoguanChina
  3. 3.Department of Computer Science & TechnologyHenan Polytechnic UniversityJiaozuoChina

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