A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 25, 2011
Edward Balaban Sriram Narasimhan Matthew Daigle José Celaya Indranil Roychoudhury Bhaskar Saha Sankalita Saha Kai Goebel

Abstract

The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision-making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.

How to Cite

Balaban, E., Narasimhan, S. ., Daigle, M. ., Celaya, J. ., Roychoudhury, I., Saha, B. ., Saha, S. ., & Goebel, K. . (2011). A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2015
Abstract 825 | PDF Downloads 284

##plugins.themes.bootstrap3.article.details##

Keywords

prognostics, testbed, decision making, autonomy

References
Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing , 50 (2), 174-189.

Balaban, E., Saxena, A., Bansal, P., Goebel, K., & Curran, S. (2009). Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications. IEEE Sensors Journal , 9 (12), 1907 - 1917.

Balaban, E., Saxena, A., Goebel, K., Byington, C., Watson, M., Bharadwaj, S., et al. (2009). Experimental Data Collection and Modeling for Nominal and Fault Conditions on Electro-Mechanical Actuators. Annual Conference of the Prognostics and Health Management Society. San Diego, CA.

Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., & Goebel, K. (2011). Experimental Validation of a Prognostic Health Management System for Electro- Mechanical Actuators. AIAA Infotech@Aerospace.

Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., Goebel, K., & Koopmans, M. (2010). Airborne Electro- Mechanical Actuator Test Stand for Development of Prognostic Health Management Systems. Annual Conference of the Prognostics and Health Management Society. San Diego, CA.

Celaya, J., Kulkarni, C., Biswas, G., & Goebel, K. (2011). Towards Prognostics of Electrolytic Capacitors. AIAA Infotech@Aerospace. St. Louis, MO.

Celaya, J., Saxena, A., Vaschenko, V., Saha, S., & Goebel, K. (2011). Prognostics of power MOSFETs. 23rd International Symposium on Power Semiconductor Devices and ICS. San Diego,CA.

Celaya, J., Saxena, A., Wysocki, P., Saha, S., & Goebel, K. (2010). Towards Prognostics of Power MOSFETs: Accelerated Aging and Precursors of Failure. Annual Conference of the Prognostics and Health Management Society. Portland,OR.

Daigle, M., & Goebel, K. (2011). Multiple Damage Progression Paths in Model-based Prognostics. IEEE Aerospace Conference. Big Sky, Montana.

Dalal, M., Estlin, T., Fry, C., Iatauro, M., Harris, R., Jonsson, A., et al. (2007). Plan Exectution Interchange Language (PLEXIL). Moffett Field: NASA Ames Research Center.
Fluckiger, L., To, V., & Utz, H. (2008). Service oriented robotic architecture supporting a lunar analog test. International Symposium on Artificial Intelligence, Robotics, and Automation in Space. Los Angeles, CA.

Frank, J., & Jonsson, A. (2003). Constraint-Based Attribute and Interval Planning. ournal of Constraints, Special Issue on Constraints and Planning , 8 (4), 335-338.
Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems. New York: Marcel Dekker Inc.

Huggins, R. (2008). Advanced Batteries: Materials Science Aspects (1st Edition ed.). Springer.

Kulkarni, C., Biswas, G., Celaya, J., & Goebel, K. (2011). Prognostic Techniques for Capacitor Degradation and Health Monitoring. Maintenance & Reliability
Conference. Knoxville, TN.

Kulkarni, C., Biswas, G., Koutsoukos, X., Celaya, J., & Goebel, K. (2010). Aging Methodologies and Prognostic Health Management for Electrolytic Capacitors. Annual Conference of the PHM Society. Portland, OR.

Lachat, D., Krebs, A., Thueer, T., & Siegwart, R. (2006). Antarctica Rover Design and Optimization for Limited Power Consumption. MECHATRONICS - 4th IFAC- Symposium on Mechatronic Systems.

Mandow, A. M.-C. (2007). Experimental kinematics for wheeled skid-steer mobile robots. Intelligent Robots and Systems, 2007 (IROS 2007). IEEE/RSJ International Conference on, (pp. 1222-1227).

Narasimhan, S., Roychoudhury, I., Balaban, E., & Saxena, A. (2010). Combining Model-based and Feature-driven Diagnosis Approaches - A Case Study on Electromechanic Actuators. 21st International Workshop on Prinicples of Diagnosis (DX 10). Portland, Oregon.

NASA Ames Research Center. (2011). The Robot Application Programming Interface Delegate Project. Retrieved from http://rapid.nasa.gov/

Object Management Group. (2004). CORBA/IIOP specification. Framingham, MA: OMG.

Patil, N. C. (2009). Precursor parameter identification for insulated gate bipolar transistor (IGBT) prognostics. IEEE Transactions on Reliability , 58(2), 271-276.

Poll, S., Patterson-Hine, A., Camisa, J., Nishikawa, D., Spirkovska, L., Garcia, D., et al. (2007). Evaluation, Selection, and Application of Model-Based Diagnosis Tools and Approaches. AIAA Infotech@Aerospace Conference and Exhibit. Rohnert Park, CA.

Rasmussen, C., & Williams, C. (2006). Gaussian Processes for Machine Learning. Boston, MA: The MIT Press.
Saha, B., & Goebel, K. (2009). Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework. Proceedings of Annual Conference of the PHM Society.
San Diego, CA.

Saha, B., Celaya, J., Wysocki, P., & Goebel, K. (2009).Towards Prognostics for Electronics Components. IEEE Aerospace, (pp. 1-7). Big Sky, MT.

Saha, B., Koshimoto, E., Quach, C., Hogge, E., Strom, T., Hill, B., et al. (2011). Predicting Battery Life for Electric UAVs. AIAA Infotech@Aerospace.

Saha, S., Celaya, J., Vashchenko, V., Mahiuddin, S., & Goebel, K. (2011). Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs. IEEE
EnergyTech, submitted .

Schmidt, D. (1994). The ADAPTIVE Communication Environment: Object-Oriented network programming components for developing client/server applications. Proceedings of the 12 th Annual Sun Users Group Conference (pp. 214–225). San Francisco, CA: SUG.

Schwabacher, M. (2005). A Survey of Data-drive Prognostics. AIAA Infotech @ Aerospace Conference.

Smith, M., Byington, C., Watson, M., Bharadwaj, S., Swerdon, G., Goebel, K., et al. (2009). Experimental and Analytical Development of a Health Management System for Electro-Mechanical Actuators. IEEE Aerospace Conference. Big Sky, MT.

Wolpert, D. (2006). Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics. (D. Braha, A. Minai, & Y. Bar- Yam, Eds.) Complex
Engineered Systems, 14, 262-290.
Section
Technical Research Papers

Most read articles by the same author(s)

1 2 3 4 5 6 7 > >>