Searching Objects in Known Environments: Empowering Simple Heuristic Strategies

  • Ramon Izquierdo-Cordova
  • Eduardo F. MoralesEmail author
  • L. Enrique Sucar
  • Rafael Murrieta-Cid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)


We consider the problem of exploring a known structured environment to find an object with a mobile robot. We proposed a novel heuristic-based strategy for reducing the traveled distance by first obtaining an exploration order of the rooms in the environment and then, searching for the object in each room by positioning the robot through a set of viewpoints. For the exploration order we proposed a heuristic based on the distance from the robot to the room, the probability of finding the object therein and the room area; integrated in a \(O(n^2)\) complexity greedy algorithm that selects the next room. The experimental results show an advantage of the proposed heuristic over other methods in terms of expected traveled distance, except for full search which has a complexity of O(n!). For the exploration within each room, we integrate the localization of horizontal flat surfaces with the generation of poses. With the set of poses, a similar heuristic establishes the exploration order that guides the robot path inside the room. The evaluation of the set of poses shows an average coverage of the flat surfaces of more than 90% when it is configured with an overlap of 40%. Experiments were performed with a real robot using three objects in a six-room environment. The success rate for the robot finding the object is 86.6%.


Service robots Object search 


  1. 1.
    Aydemir, A., Pronobis, A., Gobelbecker, M., Jensfelt, P.: Active visual object search in unknown environments using uncertain semantics. IEEE Trans. Robot. 29(4), 986–1002 (2013)CrossRefGoogle Scholar
  2. 2.
    Aydemir, A., Sjöö, K., Folkesson, J., Pronobis, A., Jensfelt, P.: Search in the real world: active visual object search based on spatial relations. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2818–2824. IEEE (2011)Google Scholar
  3. 3.
    Bellman, R.: Dynamic programming treatment of the travelling salesman problem. J. ACM (JACM) 9(1), 61–63 (1962)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cabanillas, J., Morales, E.F., Sucar, L.E.: An efficient strategy for fast object search considering the robot’s perceptual limitations. In: Kuri-Morales, A., Simari, G.R. (eds.) IBERAMIA 2010. LNCS, vol. 6433, pp. 552–561. Springer, Heidelberg (2010). Scholar
  5. 5.
    Espinoza, J., Sarmiento, A., Murrieta-Cid, R., Hutchinson, S.: Motion planning strategy for finding an object with a mobile manipulator in three-dimensional environments. Adv. Robot. 25, 1627–1650 (2011)CrossRefGoogle Scholar
  6. 6.
    Heap, B.: Permutations by interchanges. Comput. J. 6(3), 293–298 (1963)CrossRefGoogle Scholar
  7. 7.
    Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo Planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS, vol. 4212, pp. 282–293. Springer, Heidelberg (2006). Scholar
  8. 8.
    Kunze, L., Beetz, M., Saito, M., Azuma, H., Okada, K., Inaba, M.: Searching objects in large-scale indoor environments: a decision-theoretic approach. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4385–4390 (2012)Google Scholar
  9. 9.
    Rourke, J.O., Supowit, K., et al.: Some NP-hard polygon decomposition problems. IEEE Trans. Inf. Theor. 29(2), 181–190 (1983)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Sarmiento, A., Murrieta-Cid, R., Hutchinson, S.: An efficient motion strategy to compute expected-time locally optimal continuous search paths in known environments. Adv. Robot. 23, 1533–1560 (2009)CrossRefGoogle Scholar
  11. 11.
    Shade, R., Newman, P.: Choosing where to go: complete 3D exploration with stereo. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2806–2811. IEEE (2011)Google Scholar
  12. 12.
    Sjö, K., Gàlvez-López, D., Paul, C., Jensfelt, P., Kragic, D.: Object search and localization for an indoor mobile robot. J. Comput. Inf. Technol. 17, 67–80 (2009)CrossRefGoogle Scholar
  13. 13.
    Trevizan, F., Veloso, M.: Finding objects through stochastic shortest path problems. In: Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems, pp. 547–554. International Foundation for Autonomous Agents and Multiagent Systems (2013)Google Scholar
  14. 14.
    Willow Garage and ROS Community: ORK - Object Recognition Kitchen (2011).

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ramon Izquierdo-Cordova
    • 1
  • Eduardo F. Morales
    • 1
    Email author
  • L. Enrique Sucar
    • 1
  • Rafael Murrieta-Cid
    • 2
  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)TonantzintlaMexico
  2. 2.Centro de Investigación en Matemáticas, A.C. (CIMAT)GuanajuatoMexico

Personalised recommendations