Skip to main content
SpringerLink
Log in

Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

There are several works that approach the modeling of navigation environments for the automatic planning of paths for Unmanned Aerial Vehicles (UAVs). However these works do not consider the evaluation of risks to the population in case of accidents with UAVs. The present work proposes a grid-based method to construct visibility graphs based on the evaluation of effects of impacts of UAVs on the ground and on a set of geographic information. The metric used for the evaluation is the minimum acceptable period between accidents of this type. The geographic information to be used consists of georeferenced images and digital elevation models of the surfaces of the navigation environments; urban and rural population density information of the cities in these environments; and the polygonal representation of the cities. The visibility graphs constructed by the proposed method allow the UAVs to plan the shortest paths with a decrease of the probability of fatalities due to accidents with impact of UAVs on the ground. Analyses of the results are presented in this work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Dalamagkidis, K., Valavanis, K.P., Piegl, L.A.: Current status and future perspectives for unmanned aircraft system operations in the US. J. Intell. Robot. Syst. 52(2), 313–329 (2008)

    Article  Google Scholar 

  2. Dalamagkidis, K., Valavanis, K.P., Piegl, L.A.: On integrating unmanned aircraft systems into the national airspace system: issues, challenges, operational restrictions, certification, and recommendations. Springer, New York (2008)

    Google Scholar 

  3. Ludington, B., Johnson, E., Vachtsevanos, G.: Augmenting UAV autonomy: Vision-based navigation and target tracking for unmanned aerial vehicles. In: IEEE Robotics & Automation Magazine, pp. 63–71, September (2006)

  4. Grocholsky, B., Keller, J., Kumar, V., Pappas, G.: Cooperative air and ground surveillance: A scalable approach to the detection and localization of targets by a network of UAV’s and UGV’s. In: IEEE Robotics & Automation Magazine, pp. 16–26, September (2006)

  5. Tozour, P.: Search space representations. In: Rabin, S. (ed.) AI Game Programming Wisdom 2, pp. 85–102. Charles River Media (2003)

  6. Goerzen, C., Kong, Z., Mettler, B.: A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance. J. Intell. Robot. Syst. 57, 65–100 (2010)

    Article  Google Scholar 

  7. Nilsson, N.J.: A Mobile Automaton: An Application of Artificial Intelligence Techniques. In: Proceedings of the International Joint Conference on Artificial Intelligence, Association for Computing Machinery, New York, pp. 509–520 (1969)

  8. Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Contmum. ACM 22, 560–570 (1979)

    Article  Google Scholar 

  9. Lee, J., Kim, H.J.: Trajectory Generation for Rendezvous of Unmanned Aerial Vehicles with Kinematic Constraints. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1056–1061 (2007)

  10. Kito, T., Ota, J., Katsuki, R., Mizuta, T., Arai, T., Ueyama, T., Nishiyama, T.: Smooth Path Planning Using Visibility Graph-like Method. In: Proceedings of the 2003 IEEE International Conference on Robotics & Automation, pp. 14–19, September 2003, Taipei, Taiwan (2003)

  11. Kuwata, Y., How, J.P.: Stable trajectory design for highly constrained environments using receding horizon control. In: Proceedings of the 2004 American Control Conference, pp. 902–907, Boston, Massachusetts. June (2004)

  12. Dalamagkidis, K., Valavanis, K.P., Piegl, L.A.: Evaluating the risk of unmanned aircraft ground impacts. In: 16th Mediterranean conference on control and automation, pp. 709–716 (2008)

  13. Medeiros, F.L.L., Silva, J.D.S.: Grafos de Visibilidade Aplicados à Representação Computacional de Ambientes de Navegação Aérea. In: X Simpósio de Aplicações Operacionais em Áreas de Defesa (SIGE), pp. 318–320, Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos – SP, Brasil (2008)

    Google Scholar 

  14. Yap, P.: Grid-based path finding. Lecture notes in Artificial Intelligence, pp. 44–55 (2002)

  15. IBGE: Malha Municipal Digital 2005. http://www.ibge.gov.br/home/geociencias/cartografia/territ_doc1a.shtm (2009)

  16. Miranda, E.E. de, Gomes, E.G., Guimarães, M.: Mapeamento e estimativa da área urbanizada do Brasil com base em imagens orbitais e modelos estatísticos. Campinas: Embrapa Monitoramento por Satélite, 2005. http://www.urbanizacao.cnpm.embrapa.br (2009)

  17. Zaloti Junior, O.D.: Avaliação do Modelo Digital do Terreno Extraído de Dados SAR Interferométricos na Banda X do SAR R-99B. Dissertação de Mestrado do Curso de Pós-Graduação em Sensoriamento Remoto, do Instituto Nacional de Pesquisas Espaciais (2007)

  18. Richards, J.A., Jia, X.: Remote Sensing Digital Image Analisys. Springer, Berlin (2006)

    Google Scholar 

  19. ENVI: Environment for Visualizing Images. http://www.envi.com.br/ (2010)

  20. van den Boomgard, R., van Balen, R.: Methods for fast morphological image transforms using bitmapped images. CVGIP, Graph. Models Image Process. 54(3), 254–258 (1992)

    Google Scholar 

  21. Wikipedia: RQ-4 Global Hawk. http://en.wikipedia.org/wiki/RQ-4_Global_Hawk (2009)

  22. Wikipedia: MQ-1 Predator. http://en.wikipedia.org/wiki/MQ-1_Predator (2009)

  23. Wikipedia: KZO. http://en.wikipedia.org/wiki/KZO_(aircraft) (2009)

  24. Wikipedia: Apoena 3000. http://en.wikipedia.org/wiki/Apoena (2009)

  25. Sato, A.: The RMAX Helicopter UAV. Report of the Aeronautic Operations Yamaha Motor CO., LTD., Shizuoka, Japan (2003)

  26. Wikipedia: ZALA 421–08. http://en.wikipedia.org/wiki/ZALA_421–08 (2009)

  27. Medeiros, F.L.L., Shiguemori, E.H., Monteiro, M.V.T., Domiciano, M.A.P., Martins, M.P.: Verificação automática de situações de colisão na navegação de veículos aéreos não tripulados. In: Proceedings of the VI Encontro Nacional de Inteligência Artificial (ENIA’07), pp. 932–941, Rio de Janeiro, Brasil, July (2007)

  28. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MATH  MathSciNet  Google Scholar 

  29. Anderson, E.P., Beard, R.W., McLain, T.W.: Real-time dynamic trajectory smoothing for unmanned air vehicles. IEEE Trans. Control Syst. Technol. 13(3), 471–477 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto de Estudos Avançados, Rod. dos Tamoios, km 5,5, Putim, CEP 12.228-001, São José dos Campos, SP, Brazil

    Felipe Leonardo Lôbo Medeiros

  2. Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas 1.758, Jardim da Granja, CEP 12227–010, São José dos Campos, SP, Brazil

    José Demisio Simões da Silva

Authors
  1. Felipe Leonardo Lôbo Medeiros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Demisio Simões da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Felipe Leonardo Lôbo Medeiros.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Medeiros, F.L.L., Silva, J.D.S.d. Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground. J Intell Robot Syst 61, 181–202 (2011). https://doi.org/10.1007/s10846-010-9471-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-010-9471-2

Keywords

Search

Navigation