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A Localizability Constraint-Based Path Planning Method for Unmanned Aerial Vehicle

  • Behnam Irani
  • Weidong Chen
  • Jingchuan Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

As unmanned aerial vehicles (UAVs) are used in challenging environments to carry out various complex tasks, a satisfactory level of localization performance is required to ensure safe and reliable operations. 3D laser range finder (LRF)-based localization is a suitable approach in areas where GPS signal is not accesible or unreliable. During navigation, environmental information and map noises at different locations may contribute differently to a UAV’s localization process, causing it to have dissimilar ability to localize itself using LRF readings, which is referred to as localizability in this paper. We propose a localizability constraint (LC) based path planning method for UAV, which plans the navigation path according to LRF sensor model to achieve higher localization performance throughout the path. Paths planned with and without LC are compared and discussed through simulations in outdoor urban and wilderness environemnts. We show that the proposed method effectively reduces the localization error along the planned paths.

Keywords

Unmanned aerial vehicle Path planning Localizability Localization Navigation 

References

  1. 1.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Softwar. Wiley, Hoboken (2004)Google Scholar
  2. 2.
    Bobrovsky, B., Zakai, M.: A lower bound on the estimation error for markov processes. IEEE Trans. Autom. Control 20(6), 785–788 (1975)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Candido, S., Hutchinson, S.: Minimum uncertainty robot path planning using a POMDP approach. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1408–1413. IEEE (2010)Google Scholar
  4. 4.
    Censi, A.: An accurate closed-form estimate of ICP’s covariance. In: 2007 IEEE International Conference on Robotics and Automation, pp. 3167–3172. IEEE (2007)Google Scholar
  5. 5.
    Censi, A.: On achievable accuracy for range-finder localization. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 4170–4175. IEEE (2007)Google Scholar
  6. 6.
    Censi, A.: On achievable accuracy for pose tracking. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1–7. IEEE (2009)Google Scholar
  7. 7.
    Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mobile robots. In: 1999 Proceedings of IEEE International Conference on Robotics and Automation, vol. 2, pp. 1322–1328 (2002)Google Scholar
  8. 8.
    Diosi, A., Kleeman, L.: Uncertainty of line segments extracted from static SICK PLS laser scans. In: Proceedings. SICK PLS Laser. Australiasian Conference on Robotics and Automation (2003)Google Scholar
  9. 9.
  10. 10.
    Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. Robot. 28(1), 90–100 (2012)CrossRefGoogle Scholar
  11. 11.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)CrossRefGoogle Scholar
  12. 12.
    Hochstenbach, M., Notteboom, C., Theys, B., De Schutter, J.: Design and control of an unmanned aerial vehicle for autonomous parcel delivery with transition from vertical take-off to forward flight-vertikul, a quadcopter tailsitter. Int. J. Micro Air Veh. 7(4), 395–405 (2015)CrossRefGoogle Scholar
  13. 13.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots (2013). http://octomap.github.com
  14. 14.
    Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1), 99–134 (1998)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lavalle, S.M.: Rapidly-exploring random trees: progress and prospects. Algorithmic and Computational Robotics New Directions, pp. 293–308 (2001)Google Scholar
  16. 16.
    Liu, Z., Chen, W., Wang, Y., Wang, J.: Localizability estimation for mobile robots based on probabilistic grid map and its applications to localization. In: Proceedings of IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 46–51. IEEE (2012)Google Scholar
  17. 17.
    Magree, D., Johnson, E.N.: Combined laser and vision-aided inertial navigation for an indoor unmanned aerial vehicle. In: American Control Conference (ACC), pp. 1900–1905. IEEE (2014)Google Scholar
  18. 18.
    Meyer, J., Sendobry, A., Kohlbrecher, S., Klingauf, U., von Stryk, O.: Comprehensive simulation of quadrotor UAVs using ROS and Gazebo. In: 3rd International Conference on Simulation, Modeling and Programming for Autonomous Robots (SIMPAR) (2012, to appear)CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Roca, D., Martínez-Sánchez, J., Lagüela, S., Arias, P.: Novel aerial 3d mapping system based on UAV platforms and 2d laser scanners. J. Sensors 2016, 8 pages (2016)Google Scholar
  21. 21.
  22. 22.
  23. 23.
    Wang, Y., Chen, W., Wang, J., Wang, H.: Active global localization based on localizability for mobile robots. Robotica 33(08), 1609–1627 (2015)CrossRefGoogle Scholar
  24. 24.
    Zhang, F., Grocholsky, B., Kumar, V.: Formations for localization of robot networks. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 4, pp. 3369–3374. IEEE (2004)Google Scholar
  25. 25.
    Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems Conference, July 2014Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Key Laboratory of System Control and Information Processing, Department of Automation, Ministry of Education of ChinaShanghai Jiao Tong UniversityShanghaiChina

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