Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
A Comparison of Inverse Simulation-Based Fault Detection in a Simple Robotic Rover with a Traditional Model-Based Method
Authors: Murray L. Ireland, Kevin J. Worrall, Rebecca Mackenzie, Thaleia Flessa, Euan McGookin, Douglas Thomson
Abstract:
Robotic rovers which are designed to work in extra-terrestrial environments present a unique challenge in terms of the reliability and availability of systems throughout the mission. Should some fault occur, with the nearest human potentially millions of kilometres away, detection and identification of the fault must be performed solely by the robot and its subsystems. Faults in the system sensors are relatively straightforward to detect, through the residuals produced by comparison of the system output with that of a simple model. However, faults in the input, that is, the actuators of the system, are harder to detect. A step change in the input signal, caused potentially by the loss of an actuator, can propagate through the system, resulting in complex residuals in multiple outputs. These residuals can be difficult to isolate or distinguish from residuals caused by environmental disturbances. While a more complex fault detection method or additional sensors could be used to solve these issues, an alternative is presented here. Using inverse simulation (InvSim), the inputs and outputs of the mathematical model of the rover system are reversed. Thus, for a desired trajectory, the corresponding actuator inputs are obtained. A step fault near the input then manifests itself as a step change in the residual between the system inputs and the input trajectory obtained through inverse simulation. This approach avoids the need for additional hardware on a mass- and power-critical system such as the rover. The InvSim fault detection method is applied to a simple four-wheeled rover in simulation. Additive system faults and an external disturbance force and are applied to the vehicle in turn, such that the dynamic response and sensor output of the rover are impacted. Basic model-based fault detection is then employed to provide output residuals which may be analysed to provide information on the fault/disturbance. InvSim-based fault detection is then employed, similarly providing input residuals which provide further information on the fault/disturbance. The input residuals are shown to provide clearer information on the location and magnitude of an input fault than the output residuals. Additionally, they can allow faults to be more clearly discriminated from environmental disturbances.Keywords: Fault detection, inverse simulation, rover, ground robot.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129652
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 946References:
[1] S. Kassel, “Lunokhod-1 Soviet lunar surface vehicle,” DARPA, Tech. Rep. September, 1971.
[2] M. B. Quadrelli, L. J. Wood, J. E. Riedel, M. C. McHenry, M. Aung, L. A. Cangahuala, R. A. Volpe, P. M. Beauchamp, and J. A. Cutts, “Guidance, navigation and control technology assessment for future planetary science missions,” Journal of Guidance, Control, and Dynamics, vol. 38, no. 7, pp. 1165–1186, 2015.
[3] R. Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Berlin: Springer-Verlag, 2006.
[4] R. Dearden, T. Willeke, R. Simmons, V. Verma, F. Hutter, and S. Thrun, “Real-time fault detection and situational awareness for rovers: Report on the Mars technology program task,” in Proceedings of the IEEE Aerospace Conference, vol. 2, Big Sky, MT, 2004, pp. 826–840.
[5] V. Verma, G. Gordon, R. Simmons, and S. Thrun, “Tractable particle filters for rover fault diagnosis,” IEEE Robotics & Automation Magazine, vol. 11, pp. 56–66, 2004.
[6] K. Ferguson, “Towards a better understanding of the flight mechanics of compound helicopter configurations,” PhD thesis, University of Glasgow, November 2015.
[7] R. Hess, C. Gao, and S. Wang, “A generalized technique for inverse simulation applied to aircraft manoeuvres,” Journal of Guidance, Control and Dynamics, vol. 14, pp. 920–926, 1991.
[8] D. Thomson and R. Bradley, “Inverse simulation as a tool for flight dynamics research – Principles and applications,” Progress in Aerospace Sciences, vol. 42, no. 3, pp. 174–210, May 2006.
[9] D. Murray-Smith, “The inverse simulation approach: A focused review of methods and applications,” Mathematics and Computers in Simulation, vol. 53, no. 4-6, pp. 239–247, October 2000.
[10] D. J. Murray-Smith, “Inverse simulation and analysis of underwater vehicle dynamics using feedback principles,” Mathematical and Computer Modelling of Dynamical Systems, vol. 20, no. 1, pp. 45–65, 2014.
[11] D. Murray-Smith and E. McGookin, “A case study involving continuous system methods of inverse simulation for an unmanned aerial vehicle application,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 229, no. 14, pp. 2700–2717, 2015.
[12] K. Worrall, D. Thomson, and E. McGookin, “Application of inverse simulation to a wheeled mobile robot,” in Proceedings of the 6th International Conference on Automation, Robotics and Applications (ICARA 2015), Queenstown, February 2015.
[13] K. Worrall, D. Thomson, E. McGookin, and T. Flessa, “Autonomous planetary rover control using inverse simulation,” in 13th Symposium on Advanced Space Technologies in Robotics and Automation (ASTRA 2015). Noordwijk: ESA/ESTEC, May 2015.
[14] S. Rutherford and D. G. Thomson, “Improved methodology for inverse simulation,” Aeronautical Journal, vol. 100, no. 993, pp. 79–85, 1996.
[15] K. J. Worrall, “Guidance and search algorithms for mobile robots: Application and analysis within the context of urban search and rescue,” PhD thesis, University of Glasgow, 2008.
[16] K. J. Worrall and E. W. McGookin, “A mathematical model of a Lego differential drive robot,” in Proceedings of the 6th UKACC Control Conference, Glasgow, 2006.