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Diverse Planning for UAV Trajectories

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Agents and Artificial Intelligence (ICAART 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 449))

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Abstract

Nowadays, unmanned aerial vehicles (UAVs) are more and more often used to solve various tasks in both the private and the public sector. Some of these tasks can often be performed completely autonomously while others are still dependent on remote pilots. They control an UAV using a command display where they can control it manually using joysticks or give it a simple task. The command displays allow to plan the UAV trajectory through waypoints while avoiding no-fly zones. Nevertheless, the operator can be aware of other preferences or soft restrictions for which it’s not feasible to be inserted into the system especially during time critical tasks. We propose to provide the operator with several different alternative trajectories, so he can choose the best one for the current situation. In this contribution we propose several metrics to measure the diversity of the trajectories. Then we explore several algorithms for the alternative trajectories creation. Finally, we experimentally evaluate them in a benchmark 8-grid domain and we also present the evaluation by human operators.

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Notes

  1. 1.

    Additional nodes are placed on the intersection of the border and the added edge into the Delaunay graph.

References

  1. Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  2. Nash, A., Daniel, K., Koenig, S., Felner, A.: Theta*: Any-angle path planning on grids. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 1177–1183 (2007)

    Google Scholar 

  3. Yap, P.: Grid-based path-finding. In: Proceedings of the Canadian Conference on Aritificial Intelligence, pp. 44–55 (2002)

    Google Scholar 

  4. Coman, A., Muñoz-Avila, H.: Generating diverse plans using quantitative and qualitative plan distance metrics. In: AAAI. AAAI Press (2011)

    Google Scholar 

  5. Fikes, R.E., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Artif. Intell. 2, 189–208 (1971)

    Article  MATH  Google Scholar 

  6. Aurenhammer, F.: Voronoi diagrams - A survey of a fundamental geometric data structure. ACM Comput. Surv. 23, 345–405 (1991)

    Article  Google Scholar 

  7. Garrido, S., Moreno, L., Blanco, D.: Voronoi diagram and fast marching applied to path planning. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 3049–3054. IEEE (2006)

    Google Scholar 

  8. Hui-ying, D., Shuo, D., Yu, Z.: Delaunay graph based path planning method for mobile robot. In: 2010 International Conference on Communications and Mobile Computing (CMC), vol. 3, pp. 528–531 (2010)

    Google Scholar 

  9. Fortune, S.: Voronoi diagrams and Delaunay triangulations. In: Goodman, J.E., O’Rourke, J. (eds.) Handbook of Discrete and Computational Geometry, pp. 377–388. CRC Press LLC, Boca Raton (1997)

    Google Scholar 

  10. Karaman, F.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30, 846–894 (2011)

    Article  Google Scholar 

  11. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006). http://planning.cs.uiuc.edu/

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Correspondence to Jan Tožička .

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Tožička, J., Šišlák, D., Pěchouček, M. (2014). Diverse Planning for UAV Trajectories. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2013. Communications in Computer and Information Science, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44440-5_17

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  • DOI: https://doi.org/10.1007/978-3-662-44440-5_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44439-9

  • Online ISBN: 978-3-662-44440-5

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