Efficient exploration of unknown indoor environments using a team of mobile robots

  • Cyrill Stachniss
  • Óscar Martínez Mozos
  • Wolfram Burgard


Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels.


Multi-robot exploration Coordination Semantic place information 

Mathematics Subject Classification (2000)



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  1. 1.
    Albers, S., Kursawe, K., Schuierer, S.: Exloring unknown environments with obstacles. Algotithmica, 32, 123–143 (2002)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Althaus, P., Christensen, H.I.: Behaviour coordination in structured environments. Adv. Robot. 17(7), 657–674 (2003)CrossRefGoogle Scholar
  3. 3.
    Bender, M., Slonim, D.: The power of team exploration: two robots can learn unlabeled directed graphs. In: Proc. of the 35th Annual Symposium on Foundations of Computer Science, pp. 75–85, Santa Fe, 20–22 November 1994Google Scholar
  4. 4.
    Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–378 (2005)CrossRefGoogle Scholar
  5. 5.
    Cao, Y.U., Fukunaga, A.S., Khang, A.B.: Cooperative mobile robotics: antecedents and directions. J. Auton. Robots 4(1), 7–27 (1997)CrossRefGoogle Scholar
  6. 6.
    Choset, H.: Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Trans. Robot. Autom. 17(2), 125–137 (2001)CrossRefGoogle Scholar
  7. 7.
    Deng, X., Kameda, T., Papadimitriou, C.: How to learn in an unknown environment. In: Proc. of the 32nd Symposium on the Foundations of Computational Science, pp. 298–303. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  8. 8.
    Deng, X., Papadimitriou, C.: How to learn in an unknown environment: the rectilinear case. J. ACM 45(2), 215–245 (1998)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Dudek, G., Jenkin, M., Milios, E., Wilkes, D.: Robotic exploration as graph construction. IEEE Trans. Robot. Autom. 7(6), 859–865 (1991)CrossRefGoogle Scholar
  10. 10.
    Dudek, G., Jenkin, M., Milios, E., Wilkes, D.: A taxonomy for multi-agent robotics. J. Auton. Robots 3(4), 375–397 (1996)Google Scholar
  11. 11.
    Edlinger, T., von Puttkamer, E.: Exploration of an indoor-environment by an autonomous mobile robot. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1278–1248, Munich, 12–16 September 1994Google Scholar
  12. 12.
    Fox, D., Burgard, W., Kruppa, H., Thrun, S.: Collaborative multi-robot localization. In: Proc. of the 23rd German Conference on Artificial Intelligence, pp. 325–340. Springer, New York (1999)Google Scholar
  13. 13.
    Fox, D., Ko, J., Konolige, K., Stewart, B.: A hierarchical bayesian approach to the revisiting problem in mobile robot map building. In: Proc. of the Int. Symposium of Robotics Research (ISRR), Siena, 19–22 October 2003Google Scholar
  14. 14.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Gerkey, B.P., Matarić, M.J.: Sold!: auction methods for multirobot coordination. IEEE Trans. Robot. Autom. 18(5), 758–768 (2002)CrossRefGoogle Scholar
  16. 16.
    Goldberg, D., Matarić, M.J.: Interference as a tool for designing and evaluating multi-robot controllers. J. Robot. Auton. Syst. 8, 637–642 (1997)Google Scholar
  17. 17.
    Gonzalez, R.C., Wintz, P.: Digital Image Processing. Addison-Wesley, Reading (1987)Google Scholar
  18. 18.
    González-Baños, H.H., Mao, E., Latombe, J.C., Murali, T.M., Efrat, A.: Planning robot motion strategies for efficient model construction. In: Proc. Int. Symp. on Robotics Research (ISRR), pp. 345–352, Snowbird, 2000Google Scholar
  19. 19.
    Guzzoni, D., Cheyer, A., Julia, L., Konolige, K.: Many robots make short work. AI Mag. 18(1), 55–64 (1997)Google Scholar
  20. 20.
    Howard, A.: Multi-robot simultaneous localization and mapping using particle filters. In: Robotics: Science and Systems, pp. 201–208, Cambridge, 2005Google Scholar
  21. 21.
    Ko, J., Stewart, B., Fox, D., Konolige, K., Limketkai, B.: A practical, decision-theoretic approach to multi-robot mapping and exploration. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 3232–3238, Las Vegas, 2003Google Scholar
  22. 22.
    Koenig, S., Simmons, R.: Xavier: a robot navigation architecture based on partially observable markov decision process models. In: Kortenkamp, D., Bonasso, R., Murphy, R. (eds.) Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, pp. 91–122. MIT, Cambridge (1998)Google Scholar
  23. 23.
    Koenig, S., Szymanski, B., Liu, Y.: Efficient and inefficient ant coverage methods. Ann. Math. Artif. Intell. 31, 41–76 (2001)CrossRefGoogle Scholar
  24. 24.
    Koenig, S., Tovey, C., Halliburton, W.: Greedy mapping of terrain. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Seoul, 2001Google Scholar
  25. 25.
    Kuipers, B., Beeson, P.: Bootstrap learning for place recognition. In: Proc. of the National Conference on Artificial Intelligence (AAAI), Edmonton, 2002Google Scholar
  26. 26.
    Kuipers, B., Byun, Y.-T.: A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. J. Robot. Auton. Syst. 8, 47–63 (1991)CrossRefGoogle Scholar
  27. 27.
    Kurazume, R., Shigemi, N.: Cooperative positioning with multiple robots. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1250–1257, Munich, 1994Google Scholar
  28. 28.
    Lee, D., Recce, M.: Quantitative evaluation of the exploration strategies of a mobile robot. Int. J. Rob. Res. 16(4), 413–447 (1997)CrossRefGoogle Scholar
  29. 29.
    Martínez-Mozos, O., Stachniss, C., Burgard, W.: Supervised learning of places from range data using adaboost. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pp. 1742–1747, Barcelona, 2005Google Scholar
  30. 30.
    Matarić, M.J., Sukhatme, G.: Task-allocation and coordination of multiple robots for planetary exploration. In: Proc. of the Int. Conf. on Advanced Robotics (ICAR), pp. 61–70, Budapest, 2001Google Scholar
  31. 31.
    Meijster, A., Roerdink, J.B.T.M., Hesselink, W.H.: Mathematical Morphology and its Applications to Image and Signal Processing, Chapter A. General Algorithm for Computing Distance Transforms in Linear Time, pp. 331–340. Kluwer, Dordrecht (2000)Google Scholar
  32. 32.
    Oore, S., Hinton, G.E., Dudek, G.: A mobile robot that learns its place. Neural Comput. 9(3), 683–699 (1997)CrossRefGoogle Scholar
  33. 33.
    Rekleitis, I., Dudek, G., Milios, E.: Multi-robot exploration of an unknown environment, efficiently reducing the odometry error. In: Proc. of International Joint Conference in Artificial Intelligence (IJCAI), vol. 2, pp. 1340–1345 (1997)Google Scholar
  34. 34.
    Rekleitis, I., Lee-Shue, V., Peng New, A., Choset, H.: Limited communication, multi-robot team based coverage. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pp. 3462–3468, New Orleans, 2004Google Scholar
  35. 35.
    Rekleitis, I., Sim, R., Dudek, G., Milios, E.: Collaborative exploration for the construction of visual maps. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Maui, 2001Google Scholar
  36. 36.
    Roy, N., Dudek, G.: Collaborative robot exploration and rendezvous: algorithms, performance bounds and observations. J. Auton. Robots 11(2), 117–136 (2001)MATHCrossRefGoogle Scholar
  37. 37.
    Sack, D., Burgard, W.: A comparison of methods for line extraction from range data. In: Proc. of the IFAC Symposium on Intelligent Autonomous Vehicles (IAV), Lisbon, 2004Google Scholar
  38. 38.
    Schneider-Fontan, M., Matarić, M.J.: Territorial multi-robot task division. IEEE Trans. Robot. Autom. 14(5), 815–822 (1998)CrossRefGoogle Scholar
  39. 39.
    Stachniss, C.: Exploration and Mapping with Mobile Robots. PhD thesis, University of Freiburg, Department of Computer Science (2006)Google Scholar
  40. 40.
    Stachniss, C., Martínez-Mozos, O., Burgard, W.: Speeding-up multi-robot exploration by considering semantic place information. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pp. 1692–1697, Orlando, 2006Google Scholar
  41. 41.
    Stroupe, A.W., Ravichandran, R., Balch, T.: Value-based action selection for exploration and mapping with robot teams. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pp. 4090–4197, New Orleans, 2004Google Scholar
  42. 42.
    Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: Proc. of the Int. Conf. on Computer Vision (ICCV), Nice, 2003Google Scholar
  43. 43.
    Viola, P., Jones, M.J.: Robust real-time object detection. In: Proc. of IEEE Workshop on Statistical and Theories of Computer Vision, Vancouver, 2001Google Scholar
  44. 44.
    Yamauchi, B.: Frontier-based exploration using multiple robots. In: Proc. of the Second International Conference on Autonomous Agents, pp. 47–53, Minneapolis, 1998Google Scholar
  45. 45.
    Yamauchi, B., Schultz, A., Adams, W.: Integrating exploration and localization for mobile robots. Adapt. Behav. 7(2), 217–229 (1999)CrossRefGoogle Scholar
  46. 46.
    Zelinsky, A., Jarvis, R., Byrne, J., Yuta, S.: Planning paths of complete coverage of an unstructured environment by a mobile robots. In: Proc. of the Int. Conf. on Advanced Robotics (ICAR), pp. 533–538, Tokyo, 1993Google Scholar
  47. 47.
    Zlot, R., Stenz, A.T., Dias, M.B., Thayer, S.: Multi-robot exploration controlled by a market economy. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Washington, DC, 2002Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Cyrill Stachniss
    • 1
  • Óscar Martínez Mozos
    • 1
  • Wolfram Burgard
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany

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