Command and Control of Autonomous Unmanned Vehicles

Reference work entry

Abstract

Motivated by Generals Rommel and Guderian’s innovative command and control techniques used in Europe in 1940, this chapter begins by using information theory to examine unmanned air vehicle (UAV) command and control (C2). The information-theoretic analysis provides a justification and uses cases for autonomous UAVs. An autonomous unmanned vehicle system “Organic Persistent Intelligence Surveillance and Reconnaissance” (OPISR) that is designed to duplicate Guderian’s innovations is introduced. OPISR is an autonomous unmanned vehicle system that combines the immediate response to tactical ISR needs provided by organic assets with the time-on-station, minimal logistics provided by persistent unmanned systems. OPISR autonomous vehicles collectively interpret real-time tactical intelligence surveillance and reconnaissance (ISR) objectives submitted by any number of disadvantaged users, gather the required ISR data, and return the needed intelligence directly to the affected user. OPISR is an ad hoc, decentralized system that requires no central base or authority and is capable of functioning in communications-denied environment. The chapter describes a series of experiments including 2011 experiments in which 16 fully autonomous unmanned vehicles, including 9 unmanned air vehicles, were used to simultaneously support mounted, dismounted and maritime users. During these experiments users provided abstract mission-level ISR needs to the “vehicle cloud.” These needs were interpreted by the vehicles, which self-organized and efficiently achieved the user’s objectives.

Keywords

Autonomous Vehicle Ground Vehicle Unmanned Vehicle Blackboard System Truth Maintenance System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. R.C. Arkin, Governing Lethal Behavior in Autonomous Robots (CRC, Boca Raton, 2009)CrossRefGoogle Scholar
  2. R. Bamberger, R.C. Hawthorne, O. Farrag, A communications architecture for a swarm of small unmanned, autonomous air vehicles, in AUVSI’s Unmanned Systems North America Symposium, Anaheim, 3 Aug 2004Google Scholar
  3. B. Bethke, M. Valenti, J. How, Experimental demonstration of UAV task assignment with integrated health monitoring. IEEE Robot. Autom. Mag., Mar 2008Google Scholar
  4. Defense Systems Staff (2012) Army readies on-demand imagery tool for battlefield use. Defense Systems, 1 JuneGoogle Scholar
  5. R.C. Hawthorne, T. Neighoff, D. Patrone, D. Scheidt, Dynamic world modeling in a swarm of heterogeneous autonomous vehicle, in AUVSI Unmanned System North America, Aug 2004Google Scholar
  6. A.N. Kolmogorov, On tables of random numbers. Theor. Comput. Sci. 207(2), 387–395 (1998)CrossRefMATHMathSciNetGoogle Scholar
  7. V. Kumar, N. Michael, Opportunities and challenges with autonomous micro aerial vehicles. Int. J. Robot. Res. 31(11), 1279–1291 (2012)CrossRefGoogle Scholar
  8. H. Kwon, D. Pack, Cooperative target localization by multiple unmanned aircraft systems using sensor fusion quality. Optim. Lett. Spl. Issue Dyn. Inf. Syst. (Springer-Verlag) (2011)Google Scholar
  9. M. Mamei, F. Zambonelli, L. Leonardi, Co-fields: a unifying approach to swarm intelligence, in 3rd International Workshop on Engineering Societies in the Agents’ World, Madrid (E), LNAI, Sept 2002Google Scholar
  10. H. Nii, Blackboard systems. AI Mag 7(2), 38–53 (1986); 3, 82–106Google Scholar
  11. V.D. Parunak, ‘Go to the Ant’: engineering principles from natural multi-agent systems. Ann Oper Res 76, 69–101 (1997)CrossRefGoogle Scholar
  12. D. Scheidt, M. Pekala, The impact of entropic drag on command and control, in Proceedings of 12th International Command and Control Research and Technology Symposium (ICCRTS), Newport, 19–21 June 2007Google Scholar
  13. D. Scheidt, K. Schultz, On optimizing command and control, in International Command and Control Research Technology Symposium, Quebec City, June 2011Google Scholar
  14. D. Scheidt, T. Neighoff, R. Bamberger, R. Chalmers, Cooperating unmanned vehicles, in AIAA 3rd “Unmanned Unlimited” Technical Conference, Chicago, 20 Sept 2004Google Scholar
  15. D. Scheidt, T. Neighoff, J. Stipes, Cooperating unmanned vehicles, in IEEE International Conference on Networking, Sensing and Control, Tuscon, 19–22 Mar 2005Google Scholar
  16. C. E. Shannon, A mathematical theory of communications. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)CrossRefMATHMathSciNetGoogle Scholar
  17. J. Stipes, D. Scheidt, R.C. Hawthorne, Cooperating unmanned vehicles, in International Conference on Robotics and Automation, Rome, 10 Apr 2007Google Scholar
  18. J. Tisdale, Z. Kim, K. Hedrick, An autonomous system for cooperative search and localization using unmanned vehicles, in Proceedings of the AIAA Guidance, Navigation and Control Conference, Honolulu, Aug 2008Google Scholar
  19. J.A. Wheeler, Information, physics, quantum: the search for links, complexity, entropy and the physics of information, in A Proceedings Volume in the Sante Fe Institute Studies in the Sciences of Complexity, ed. by W.H. Zurek (Westview Press, 1990)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Johns Hopkins University Applied Physics LaboratoryLaurelUSA

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