International Journal of Aeronautical and Space Sciences

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Waypoint Planning Algorithm Using Cost Functions for Surveillance

  • Lim, Seung-Han (Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology) ;
  • Bang, Hyo-Choong (Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology)
  • Published : 2010.06.15
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Abstract

This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

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References

  1. Bertuccelli, L. F. and How, J. P. (2005). Robust UAV search for environments with imprecise probability maps. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, Seville, Spain. pp. 5680-5685.
  2. Bertuccelli, L. F. and How, J. P. (2006). Search for dynamic targets with uncertain probability maps. Proceedings of the American Control Conference, Minneapolis, MN. pp. 737-742.
  3. Flint, M., Polycarpou, M., and Fernandez-Gaucherand, E. (2002a). Cooperative control for multiple autonomous UAV’s searching for targets. Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, NV. pp. 2823-2828.
  4. Flint, M., Polycarpou, M., and Fernandez-Gaucherand, E. (2002b). Cooperative path-planning for autonomous vehicles using dynamic programming. Proceedings of the 15th IFAC World Congress, Barcelona, Spain. https://doi.org/10.1109/CDC.2002.1184950
  5. Lim, S. and Bang, H. (2009). Waypoint guidance of cooperative UAVs for intelligence, surveillance, and reconnaissance. IEEE International Conference on Control and Automation, Christchurch. pp. 291-296. https://doi.org/10.1109/ICCA.2009.5410404
  6. Moon, S. W. and Shim, H. C. (2009). Study on path planning algorithm for unmanned agricultural helicopters in complex environment. KASA International Journal, 10, 1-11.
  7. Polycarpou, M. M., Yang, Y., and Passino, K. M. (2001). A cooperative search framework for distributed agents. IEEE International Symposium on Intelligent Control - Proceedings, Mexico City, Mexico. pp. 1-6. https://doi.org/10.1109/ISIC.2001.971475
  8. Russell, S. J. and Norvig P. (2003). Artificial Intelligence: A Modern Approach. 2nd ed. Upper Saddle River, NJ: Prentice Hall/Pearson Education.
  9. Spires, S. V. and Goldsmith, S. Y. (1998). Exhaustive geographic search with mobile robots along space-filling curves. Proceedings of the First International Workshop on Collective Robotics. pp. 1-12.
  10. Stone, L. D. (1975). Theory of Optimal Search. New York: Academic Press.
  11. Sujit, P. B. and Ghose, D. (2003). Optimal uncertainty reduction search using the k-shortest path algorithm. Proceedings of the American Control Conference, Denver, CO. pp. 3269-3274. https://doi.org/10.1109/ACC.2003.1244035
  12. Sujit, P. B. and Ghose, D. (2004a). Multiple agent search of an unknown environment using game theoretical models. Proceedings of the American Control Conference, Boston, MA. pp. 5564-5569.
  13. Sujit, P. B. and Ghose, D. (2004b). Search using multiple UAVS with flight time constraints. IEEE Transactions on Aerospace and Electronic Systems, 40, 491-509. https://doi.org/10.1109/TAES.2004.1310000
  14. Yang, Y., Minai. A. A., and Polycarpou, M. M. (2002a). Decentralized cooperative search in UAVs using opportunistic learning. Proceedings of the AIAA Guidance, Navigation and Control Conference, Monterey, CA.
  15. Yang, Y., Polycarpou, M. M., and Minai, A. A. (2002b). Opportunistically cooperative neural learning in mobile agents. Proceedings of the International Joint Conference on Neural Networks, Honolulu, HI. pp. 2638-2643.
  16. Yang, Y., Minai, A. A., and Polycarpou, M. M. (2004). Decentralized cooperative search by networked UAVs in an uncertain environment. Proceedings of the American Control Conference, Boston, MA. pp. 5558-5563.

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