Advertisement

Cooperative Control for Autonomous Air Vehicles

  • Kevin Passino
  • Marios Polycarpou
  • David Jacques
  • Meir Pachter
  • Yang Liu
  • Yanli Yang
  • Matt Flint
  • Michael Baum
Chapter
Part of the Applied Optimization book series (APOP, volume 66)

Abstract

The main objective of this research is to develop and evaluate the performance of strategies for cooperative control of autonomous air vehicles that seek to gather information about a dynamic target environment, evade threats, and coordinate strikes against targets. The air vehicles are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. They are assumed to have some “physical” limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (i) on-line learning of the environment and storing of the information in the form of a “target search map”; and (ii) utilization of the target search map and other information to compute on-line a guidance trajectory for the vehicle to follow. We study the stability of vehicular swarms to try to understand what types of communications are needed to achieve cooperative search and engagement, and characteristics that affect swarm aggregation and disintegration. Finally, we explore the utility of using-biomimicry of social foraging strategies to develop coordination strategies.

Keywords

cooperative control autonomous air vehicles 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Ablavsky, V. and Snorrason, M. (2000). Optimal search for a moving target: a geometric approach. In AIAA Guidance, Navigation, and Control Conference and Exhibit, Denver, CO.Google Scholar
  2. [2]
    Benkoski, S., Monticino, M., and Weisinger, J. (1991). A survery of the search theory literature. Naval Research Logistics, 38:469–494.zbMATHGoogle Scholar
  3. [3]
    Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford Univ. Press, NY.zbMATHGoogle Scholar
  4. [4]
    Cameron, S. (1994). Obstacle avoidance and path planning. Industrial Robot, 21:9–14.CrossRefGoogle Scholar
  5. [5]
    Choset, H. and Pignon, P. (1997). Coverage path planning: the boustrophedon cellular decomposition. In International Conference on Field and Service Robotics, Canberra, Australia.Google Scholar
  6. [6]
    Conn, A., Schcinberg, K., and Toint, P. (1997). Recent progress in unconstrainted nonlinear optimization without derivatives. Mathematical Programming, 79:397–414.CrossRefMathSciNetGoogle Scholar
  7. [7]
    Danskin, J. (1968). A helicopter versus submarines search game. Operations Research, 16:509–517.Google Scholar
  8. [8]
    Dell, R. and Eagle, J. (1996). Using multiple searchers in constrainted-path moving-targer search problems. Naval Research Logistics, 43:463–480.CrossRefzbMATHGoogle Scholar
  9. [9]
    Dennis, J. and Torczon, V. (1991). Direct search methods on parallel machines. SIAM Journal Optimization, 1:448–474.MathSciNetzbMATHGoogle Scholar
  10. [10]
    Eagle, J. and Yee, J. (1990). An optimal branch-and-bound procedure for the constrained path moving target search problem. Operations Research, 38:11–114.MathSciNetGoogle Scholar
  11. [11]
    Gillen, D. and Jacques, D. (2000). Cooperative behavior schemes for improving the effectiveness of autonomous wide area search munitions. In Proceedings of Workshop on Cooperative Control and Optimization, University of Florida, Gainesville.Google Scholar
  12. [12]
    Goldsmith, S. and Robinett, R. (1998). Collective search by mobile robots using alpha-beta coordination. In Drogoul, A., Tambe, M., and Fukuda, T., editors, Collective Robotics, pages 136–146. Springer Verlag: Berlin.Google Scholar
  13. [13]
    Hert, S., Tiwari, S., and Lumelsky, V. (1996). A terrain-covering algorithm for auv. Autonomous Robots, 3:91–119.CrossRefGoogle Scholar
  14. [14]
    Hohzaki, R. and Iida, K. (1995a). An optimal search plan for a moving target when a search path is given. Mathematica Japonica, 41:175–184.MathSciNetzbMATHGoogle Scholar
  15. [15]
    Hohzaki, R. and Iida, K. (1995b). Path constrained search problem with reward criterion. Journal of the Operations Research Society of Japan, 38:254–264.MathSciNetzbMATHGoogle Scholar
  16. [16]
    Hohzaki, R. and Iida, K. (2000). A search game when a search path is given. European Journal of Operational Reasearch, 124:114–124.MathSciNetzbMATHGoogle Scholar
  17. [17]
    Jacques, D. and Leblanc, R. (1998). Effectiveness analysis for wide area search munitions. In Proceedings of the AIAA Missile Sciences Conference, Monterey, CA.Google Scholar
  18. [18]
    Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. In International Conference on Robotics and Automation, pages 500–505, St. Louis.Google Scholar
  19. [19]
    Koopman, B. (1980). Search and Screening: General principles with Historical Application Pergarnon, New York.zbMATHGoogle Scholar
  20. [20]
    Lucidi, S. and Sciandrone, M. (1997). On the global convergence of derivative free methods for unconstrained optimization. In Technical Report. Univ.di Roma.Google Scholar
  21. [21]
    Madigan, M., Martinko, J., and Parker, J. (1997). Biology of Microorganisms. Prentice Hall, NJ, 8 edition.Google Scholar
  22. [22]
    Nelder, J. and Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7:308–313.zbMATHGoogle Scholar
  23. [23]
    Passino, K. and Burgess, K. (1998). Stability Analysis of Discrete Event Systems. John Wiley and Sons Pub., New York.Google Scholar
  24. [24]
    Passino, K. M. (2001). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine (to appear)Google Scholar
  25. [25]
    Richardson, H. (1987). Search theory. In Center for Naval Analyses. NOO-014-83-C-0725.Google Scholar
  26. [26]
    Snorrason, M. and Norris, J. (1999). Vision based obstacle detection and path planetary rovers. In Unmanned Ground Vehicle Technology II, Orlanso, FL.Google Scholar
  27. [27]
    Spires, S. and Goldsmith, S. (1998). Exhaustive geographic search with mobile robots along space-filling curves. In Drogoul, A., Tambe, M., and Fukuda, T., editors, Collective Robotics, pages 1–12. Springer Verlag: Berlin.Google Scholar
  28. [28]
    Stephens, D. and Krebs, J. (1986). Foraging Theory. Princeton Univ. Press, Princeton, NJ.Google Scholar
  29. [29]
    Stewart, T. (1980). Experience with a branch-and-bound algorithm for constrained searcher motion. In Haley, K. and Stone, L., editors, Search Theory and Applications, pages 247–253. Plenum Press, New York.Google Scholar
  30. [30]
    Stone, L. (1975). Theory of Optimal Search. Acadamic Press, New York.zbMATHGoogle Scholar
  31. [31]
    Stone, L. (1983). The process of search planning: Current approachs and the continuing problems. Operational Research, 31:207–233.Google Scholar
  32. [32]
    Torczon, V. (1991). On the convergence of the multidirectional search algorithm. SIAM Journal Optimization, 1:123–145.MathSciNetzbMATHGoogle Scholar
  33. [33]
    Torczon, V. (1997). On the convergence of pattern search algorithms. SIAM Journal Optimization, 7:1–25.MathSciNetzbMATHGoogle Scholar
  34. [34]
    Tsitsiklis, J. and Bertsekas, D. (1989). Parallel and Distributed Computation. Prentice-Hall, Inc., Engelwood Cliffs, NJ.zbMATHGoogle Scholar
  35. [35]
    Washburn, A. (1980). Search-evasion game in a fixed region. Operations Research, 28:1290–1298.MathSciNetzbMATHCrossRefGoogle Scholar
  36. [36]
    Weaver, S., Baird, L., and Polycarpou, M. (1998). An analytical framework for local feedforward networks. IEEE Transactions on Neural Networks, 9(3):473–482.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Kevin Passino
    • 1
  • Marios Polycarpou
    • 2
  • David Jacques
    • 3
  • Meir Pachter
    • 3
  • Yang Liu
    • 1
  • Yanli Yang
    • 2
  • Matt Flint
    • 2
  • Michael Baum
    • 1
  1. 1.Department of Electrical EngineeringThe Ohio State UniversityColumbusUSA
  2. 2.Dept. of Electrical and Computer Engineering and Computer SciencesUniversity of CincinnatiCincinnatiUSA
  3. 3.Air Force Institute of TechnologyAFIT/ENY Wright Patterson Air Force BaseUSA

Personalised recommendations