Advertisement

Adapting Real-Time Path Planning to Threat Information Sharing

  • Zheng Zheng
  • Xiaoyi Zhang
  • Wei Liu
  • Wenlong Zhu
  • Peng Hao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 214)

Abstract

Information sharing is an important characteristic of cooperative flights, bringing forward new challenges to real-time path planning approaches. In this paper, problems brought out by threats information sharing are proposed, while distinguishing characteristics of threats environment with information sharing are analyzed. Based on these, solutions to these characteristics and a new path planning approach based on three strategies are advanced. At last, to test the applicability of the proposed approach, it is implemented to the improvement of a real-time Rapidly-exploring Random Tree (RRT) algorithm. The effectiveness and efficiency of the resultant algorithm are verified by stochastic and representative scenarios, which show that the new approach can be more adaptive to the threat information sharing environments.

Keywords

Path planning Real-time path planning Multi-UAVs Information sharing 

References

  1. 1.
    Liao Y, Jin Y, Minai AA, Polycarpou MM (2005) Information sharing in cooperative unmanned aerial vehicle teams. In: Proceedings of IEEE conference on decision and control, IEEE Press, Seville, pp 90–95Google Scholar
  2. 2.
    Ren W, Beard RW, McLain TW (2004) Coordination variables and consensus building in multiple vehicle systems. In: Kumar V, Leonard N, Morse AS (eds) Cooperative control. Springer, Heidelberg, pp 171–188Google Scholar
  3. 3.
    United States Air Force (2009) Unmanned aircraft systems flight plan 2009–2047. Technical report, Headquarters, United States Air ForceGoogle Scholar
  4. 4.
    Kim Y, Gu DW, Postlethwaite I (2007) Real-time optimal mission scheduling and flight path selection. IEEE Trans Autom Control 52:1119–1123Google Scholar
  5. 5.
    Kim Y, Gu DW, Postlethwaite I (2008) Real-time path planning with limited information for autonomous unmanned air vehicles. Automatica 44:696–712MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zheng Z, Wu SJ, Liu W, Cai KY (2011) A feedback based CRI approach to fuzzy reasoning. Appl Soft Comput 11:1241–1255CrossRefGoogle Scholar
  7. 7.
    Wu SJ, Zheng Z, Cai KY (2011) Real-time path planning for unmanned aerial vehicles using behavior coordination and virtual goal. Control Theory Appl 28:131–136Google Scholar
  8. 8.
    LaValle SM (2006) Planning algorithms. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  9. 9.
    Gu DW, Kamal W, Postlethwaite I (2004) A UAV waypoint generator. In: Proceedings of AIAA 1st Intelligent systems technical conference. AIAA Press, Chicago, pp 1–6Google Scholar
  10. 10.
    LaValle SM, Kuffner JJ (2001) Rapidly-exploring random trees: progress and prospects. In: Donald BR, Lynch KM, Rus D, Wellesley E (eds) Algorithmic and computational robotics: new directions. A K Peters, pp 293–308Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Zheng Zheng
    • 1
  • Xiaoyi Zhang
    • 1
  • Wei Liu
    • 1
  • Wenlong Zhu
    • 1
  • Peng Hao
    • 1
  1. 1.Department of Automatic ControlBeijing University of Aeronautics and AstronauticsBeijingChina

Personalised recommendations