Dynamic Trajectory Replanning for Unmanned Aircrafts Supporting Tactical Missions in Urban Environments

  • Lukáš Chrpa
  • Peter Novák
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6867)

Abstract

In the last decade we witnessed an increased demand for employment of unmanned aerial vehicles (UAV) in practise. For instance, there is a growing need to provide surveillance tasks in a given area by a team of cooperating UAVs. In this case, the ability of a single UAV to plan its course of actions (e.g., trajectories that the UAV must fly through) is essential. Trajectory planning algorithm used by UAVs must be able to find trajectories satisfying constraints given by environment (e.g., obstacles) or by UAVs’ dynamic models. Besides the planner itself the UAVs must somehow react to changes of high-level tasks or environment. Such a reaction often means to replan the trajectories towards new goals. In this paper, we will discuss the replanning related issues such as swapping the old and new trajectory smoothly respecting the UAV dynamics. We present an idea based on estimating running time of replanning tasks and evaluated its impact to safeness of replanning (e.g., avoiding to get to an inconsistent state).

Keywords

Unmanned Aerial Vehicle Task Allocation Trajectory Planning Dynamic Trajectory Safeness Function 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chrpa, L.: Trajectory planning on grids: Considering speed limit constraints. In: Proceedings of SCAI, pp. 60–69. IOS Press, Amsterdam (2011)Google Scholar
  2. 2.
    Chrpa, L., Komenda, A.: Smoothed hex-grid trajectory planning using helicopter dynamics. In: Proceedings of ICAART, vol. 1, pp. 629–632 (2011)Google Scholar
  3. 3.
    Dinont, C., Mathieu, P., Druon, E., Taillibert, P.: Artifacts for time-aware agents. In: Proceedings of AAMAS, pp. 593–600 (2006)Google Scholar
  4. 4.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  5. 5.
    Jakob, M., Semsch, E., Pavlíček, D., Pěchouček, M.: Occlusion-aware multi-uav surveillance of multiple urban areas. In: 6th Workshop on Agents in Traffic and Transportation (ATT 2010), pp. 59–66 (2010)Google Scholar
  6. 6.
    Nigam, N., Kroo, I.: Persistent surveillance using multiple unmanned air vehicles. In: Proceedings of the IEEE Aerospace Conference, pp. 1–14 (2008)Google Scholar
  7. 7.
    Šišlák, D., Volf, P., Pěchouček, M.: Accelerated a* path planning. In: Proceedings of AAMAS (2), pp. 1133–1134 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lukáš Chrpa
    • 1
  • Peter Novák
    • 1
  1. 1.Agent Technology Center Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic

Personalised recommendations