A Self-Organization Strategy for Unmanned Autonomous Systems
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Published
Jul 14, 2017
Benjamin Lee
Sehwan Oh
Michael Balchanos
George Vachtsevanos
Abstract
Complex systems are constructed from multiple subsystems
and components with each serving incremental tasks, where
the “emergent” system behavior cannot be deduced from the
behaviors of the individual parts. The key requirement of
complex systems is the ability to compensate for unforeseen
and extreme disturbances, so it is important to design a
control method that ensures acceptable level of system
resilience throughout its operation. Therefore, detailed and
accurate knowledge of system behaviors is paramount for the
design of complex system control strategies. This paper
presents a self-organizing control strategy that incorporates
both situational awareness and failure impact compensation
for a resilient unmanned autonomous system.
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References
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Jeong W., Kim H., Kim S. & Jun B. (2013), Path Tracking Controller Design of Hexapod Robot for Omnidirectional Gaits, Control Conference (ASCC), 2013 9th Asian, 23-26 June 2013, DOI: 10.1109/ASCC.2013.6606206
Prehofer C. & Bettstetter C. (2005), Self-organization in communication networks: principles and design paradigms, IEEE Communications Magazine, July 25, DOI: 10.1109/MCOM.2005.1470824
Heylighen F. (1999), The Science of Self-Organization and Adaptivity, The Encyclopedia of Life Support Systems, Vol. 5 (No. 3), 253-280
Wilson R. (1996). Introduction to Graph Theory. Edinburgh Gate, Harlow, Essex CM20 2JE, England: Longman.
Liu W., Sirisena H., Pawlikowski K. & McInnes A. (2009), Utility of algebraic connectivity metric in topology design of survivable networks, Design of Reliable Communication Networks, 2009. DRCN 2009. 7th International Workshop, 25-28 Oct. 2009, DOI: 10.1109/DRCN.2009.5340016
Butler S. K., (2008). Eigenvalues and Structures of Graphs. Doctoral dissertation. University of California, San Diego, http://orion.math.iastate.edu/butler/PDF/dissertation.pdf
Bellman R. (1957), A Markovian Decision Process, Indiana University Mathematics Journal, Vol. 6 (No. 4), 679-684
Yukalov V.I. & Sornette D. (2014), Self-organization in complex systems as decision making, Adv. Complex Syst., 17 (2014) 1450016
Gabbai J., (2005). Complexity and the Aerospace Industry: Understanding Emergence by Relating Structure to Performance using Multi-Agent Systems. Doctoral dissertation. University of Manchester, http://gabbai.com/files/J%20M%20E%20Gabbai%20EngD%20Thesis.pdf
Cully A., Clune J., Tarapore D. & Mouret J. (2015), Robots that can adapt like animals, Nature, Vol. 521 (No. 7553), 503-507, doi:10.1038/nature14422
Sorin M., Mircea N. & Viorel S. (2011), Hexapod robot: Mathematical support for modeling and control, System Theory, Control, and Computing (ICSTCC), 2011 15th International Conference, Oct 14-16
Barai R., Saha P. & Mandal A. (2013), SMART-HexBot: a Simulation, Modeling, Analysis and Research Tool for Hexapod Robot in Virtual Reality and Simulink, AIR '13 Proceedings of Conference on Advances In Robotics, doi>10.1145/2506095.2506126
Yang J. (2003), Fault-tolerant gait generation for locked joint failures, Systems, Man and Cybernetics, 2003. IEEE International Conference, Oct. 8, DOI: 10.1109/ICSMC.2003.1244216
Cuaya-Simbro G. & Munoz-Melendez A. (2008), Adaptive Locomotion for a Hexagonal Hexapod Robot Based on a Hierarchical Markov Decision Process, WSPC – Proceedings, Vol. 0 (No. 12), June 2008
Chades I., Chapron G., Cros MJ., Garcia F., Sabbadin R. (2014). MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems. Ecography 37:916-920.
Jeong W., Kim H., Kim S. & Jun B. (2013), Path Tracking Controller Design of Hexapod Robot for Omnidirectional Gaits, Control Conference (ASCC), 2013 9th Asian, 23-26 June 2013, DOI: 10.1109/ASCC.2013.6606206
Section
Regular Session Papers