A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications



Liang Tang Eric Hettler Bin Zhang Jonathan DeCastro


Autonomous unmanned vehicles are playing an increasingly important role in support of a wide variety of present and future critical missions. Due to the absence of timely pilot interaction and potential catastrophic consequence of unattended faults and failures, a real-time, onboard health and contingency management system is desired. This system would be capable of detecting and isolating faults, predicting fault progression and automatically reconfiguring the system to accommodate faults. This paper presents a robotic testbed that was developed for the purpose of developing and evaluating real-time PHM and Automated Contingency Management (ACM) techniques on autonomous vehicles. The testbed hardware is based on a Pioneer 3-AT robotic platform from Mobile Robots, Inc. and has been modified and enhanced to facilitate the simulations of select fault modes and mission-level applications. A hierarchical PHM-enabled ACM system is being developed and evaluated on the testbed to demonstrate the feasibility and benefit of using PHM information in vehicle control and mission reconfiguration. Several key software modules including a HyDE-based diagnosis reasoner, particle filtering-based prognosis server and a prognostics-enhanced mission planner are presented in this paper with illustrative experimental results. This testbed has been developed in hope of accelerating related technology development and raising the Technology readiness level(TRL)ofemergingACM techniques for autonomous vehicles.

How to Cite

Tang, L., Hettler, E. ., Zhang, B. ., & DeCastro, J. . (2011). A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2018
Abstract 17 | PDF Downloads 20



control reconfiguration, fault accommodation, PHM, testbed, unmanned systems, robotics

Army UAS Center of Excellence Fort Rucker AL (2010), U.S. Army Roadmap for Unmanned Aircraft Systems 2010-2035: Eyes of the Army, http://www.rucker.army.mil/usaace/uas/US%20Army%20UAS %20RoadMap%202010%202035.pdf.

DeCastro, J. A., Tang, L., Zhang, B. and Vachtsevanos, G. (2011), A Safety Verification Approach to Fault- Tolerant Aircraft Supervisory Control, in proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit.

Edwards, D., Orchard, M., Tang, L., Goebel, K., and V achtsevanos, G. (2010), Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems, in proceedings of the Annual Conference of the Prognostics and Health Management Society, Portland, OR.

Goebel, K., Saha, B., Saxena, A., Celaya, J., and Christophersen, J. (2008), Prognostics in Battery Health Management, Instrumentation & Measurement Magazine, IEEE , vol.11, no.4, pp.33- 40.

Navy's Program Executive Officer Littoral and Mine Warfare, Surface Warfare and Unmanned Maritime Vehicles Program Office (2007), The Navy Unmanned Surface V ehicle (USV) Master Plan, http://www.navy.mil/navydata/technology/usvmppr. pdf

Narasimhan, S. and Brownston, L.(2007), HyDE – A General Framework for Stochastic and Hybrid Model-based Diagnosis, in proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07).

Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., and V achtsevanos, G., (2008). Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics, in proceedings of International Conference on Prognostics and Health Management.

Orchard, M., Tang, L., Saha, B., Goebel, K., and Vachtsevanos, G., (2010) Risk-Sensitive ParticleFiltering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices, Studies in Informatics and Control, vol. 19, Issue 3, pp. 209-218.

Saha, B., and Goebel, K. (2007). "Battery Data Set", NASA Ames Prognostics Data Repository, [http://ti.arc.nasa.gov/project/prognostic-data- repository], NASA Ames, Moffett Field, CA.

Saha, B., Goebel, K., Poll, S. and Christophersen, J. (2009), Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework, IEEE Transactions on Instrumentation and Measurement, vol.58, no.2, pp.291-296.

Saxena, A., Celaya, J., Saha, B., Saha, S., and Goebel, K. (2010), Metrics for Offline Evaluation of Prognostics Performance, International Journal of Prognostics and Health Management, Vol.1(1), pp. 20.

Schwabacher, M. and Goebel, K. (2007), A Survey of Artificial Intelligence for Prognostics, in Proceedings of AAAI Fall Symposium, Arlington, VA.

Tang, L., Kacprzynski, G. J., Goebel, K., Saxena, A., Saha, B., and Vachtsevanos, G. (2008). Prognostics- enhanced Automated Contingency Management for Advanced Autonomous Systems, in Proceedings of the International Conference on Prognostics and Health Management.

Tang, L., Kacprzynski, G., J., Goebel, K., and V achtsevanos, G. (2010). Case Studies for Prognostics-Enhanced Automated Contingency Management for Aircraft Systems, in Proceedings of IEEE Aerospace Conference.

Uckun, S., Goebel, K., and Lucas, P. J. F. (2008), Standardizing Research Methods for Prognostics, in proceeding of the 1st International Conference on Prognostics and Health Management (PHM08), Denver CO.

Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A. & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1st ed. Hoboken, New Jersey: John Wiley & Sons, Inc,.

Zhang, B., Tang, L., Decastro, J. A., and Goebel, K. (2011), Prognostics-enhanced Receding Horizon Mission Planning for Field Unmanned Vehicles, in proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit.
Technical Papers

Most read articles by the same author(s)

1 2 > >>