Towards Optimal Robot Navigation in Domestic Spaces

  • Rodrigo Ventura
  • Aamir Ahmad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8992)


The work presented in this paper is motivated by the goal of dependable autonomous navigation of mobile robots. This goal is a fundamental requirement for having autonomous robots in spaces such as domestic spaces and public establishments, left unattended by technical staff. In this paper we tackle this problem by taking an optimization approach: on one hand, we use a Fast Marching Approach for path planning, resulting in optimal paths in the absence of unmapped obstacles, and on the other hand we use a Dynamic Window Approach for guidance. To the best of our knowledge, the combination of these two methods is novel. We evaluate the approach on a real mobile robot, capable of moving at high speed. The evaluation makes use of an external ground truth system. We report controlled experiments that we performed, including the presence of people moving randomly nearby the robot. In our long term experiments we report a total distance of 18 km traveled during 11 h of movement time.


Path Planning Obstacle Avoidance Robot Navigation Eikonal Equation Navigation Algorithm 
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.


  1. 1.
    Sethian, J.A.: Fast marching methods. SIAM Rev. 41(2), 199–235 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)CrossRefGoogle Scholar
  3. 3.
    Cakmak, M., Takayama, L.: Towards a comprehensive chore list for domestic robots. In: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 93-94 March 2013Google Scholar
  4. 4.
    Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: a survey. Robot. Auton. Syst. 61(12), 1726–1743 (2013)CrossRefGoogle Scholar
  5. 5.
    Kruse, T., Kirsch, A., Sisbot, E., Alami, R.: Exploiting human cooperation in human-centered robot navigation. In: RO-MAN, pp. 192–197. IEEE September 2010Google Scholar
  6. 6.
    Yuan, F., Twardon, L., Hanheide, M.: Dynamic path planning adopting human navigation strategies for a domestic mobile robot. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3275–3281, October 2010Google Scholar
  7. 7.
    Stuckler, J., Behnke, S.: Following human guidance to cooperatively carry a large object. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 218–223, October 2011Google Scholar
  8. 8.
    Klaess, J., Stueckler, J., Behnke, S.: Efficient mobile robot navigation using 3D surfel grid maps. In: 7th German Conference on Robotics; Proceedings of ROBOTIK 2012, pp. 1–4, May 2012Google Scholar
  9. 9.
    Stückler, J., Droeschel, D., Gräve, K., Holz, D., Kläß, J., Schreiber, M., Steffens, R., Behnke, S.: Towards robust mobility, flexible object manipulation, and intuitive multimodal interaction for domestic service robots. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds.) RoboCup 2011. LNCS, vol. 7416, pp. 51–62. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  10. 10.
    Holz, D., Kraetzschmar, G.K., Rome, E.: Robust and computationally efficient navigation in domestic environments. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS, vol. 5949, pp. 104–115. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  11. 11.
    Jacobs, S., Ferrein, A., Schiffer, S., Beck, D., Lakemeyer, G.: Robust collision avoidance in unknown domestic environments. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS, vol. 5949, pp. 116–127. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  12. 12.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge Monograph on Applied and Computational Mathematics. Cambridge Press, Cambridge (1999) zbMATHGoogle Scholar
  13. 13.
    Garrido, S., Moreno, L., Abderrahim, M., Blanco, D.: FM2: a real-time sensor-based feedback controller for mobile robots. Int. J. Robot. Autom. 24(1), 48–65 (2009)Google Scholar
  14. 14.
    Brock, O., Khatib, O.: High-speed navigation using the global dynamic window approach. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 341–346. IEEE (1999)Google Scholar
  15. 15.
    Messias, J., Ventura, R., Lima, P., Sequeira, J., Alvito, P., Marques, C., Carrico, P.: A robotic platform for edutainment activities in a pediatric hospital. In: Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions (2014) (accepted)Google Scholar
  16. 16.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)CrossRefGoogle Scholar
  17. 17.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005) zbMATHGoogle Scholar
  18. 18.
    Ahmad, A., Xavier, J., Santos-Victor, J., Lima, P.: 3D to 2D bijection for spherical objects under equidistant fisheye projection. Comput. Vis. Image Underst. 125, 172–183 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST)LisbonPortugal

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