Skip to main content
Log in

Local map-based exploration using a breadth-first search algorithm for mobile robots

  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

This paper describes a local map-based exploration strategy. Segmented frontiers and relative transformations constitute a tree structure. This frontier tree structure, which manages multiple local maps, effectively overcomes the limitations of conventional exploration methods, which maintain only a single global map. Although this method uses only local maps and adjacent node information, mapping completion and efficiency can be improved greatly by merging and updating the frontier nodes. In addition, a modified breadth-first search (BFS) algorithm is used to determine the next exploration target. BFS exploration is appropriate for large environments because it induces a loop-closing event from the root node, which is necessary to recover the estimation accuracy when the uncertainty of the robot’s pose has become large. The proposed local map-based BFS exploration can construct an accurate map, even in large environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Blum, A., Raghavan, P., and Schieber, B., “Navigating in Unfamiliar Geometric Terrain,” SIAM Journal on Computing, Vol. 26, No. 1, pp. 110–137, 1997.

    Article  MathSciNet  MATH  Google Scholar 

  2. Deng, X., Kameda, T., and Papadimitriou, C., “How to Learn an Unknown Environment. I: The Rectilinear Case,” Journal of the ACM (JACM), Vol. 45, No. 2, pp. 215–245, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  3. Dudek, G., Jenkin, M., Milios, E., and Wilkes, D., “Robotic Exploration as Graph Construction,” IEEE Transactions on Robotics and Automation, Vol. 7, No. 6, pp. 859–865, 1991.

    Article  Google Scholar 

  4. Panaite, P. and Pelc, A., “Exploring Unknown Undirected Graphs,” Proc. of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 316–322, 1998.

    Google Scholar 

  5. Marinakis, D. and Dudek, G., “Pure Topological Mapping in Mobile Robotics,” IEEE Transactions on Robotics, Vol. 26, No. 6, pp. 1051–1064, 2010.

    Article  Google Scholar 

  6. Cabrera-Mora, F. and Jizhong, X., “A Flooding Algorithm for Multirobot Exploration,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 42, No. 3, pp. 850–863, 2012.

    Article  Google Scholar 

  7. Moravec, H. P. and Elfes, A., “High Resolution Maps from Wide Angle Sonar,” Proc. of IEEE International Conference on Robotics and Automation, Vol. 2, pp. 116–121, 1985.

    Google Scholar 

  8. Yamauchi, B., “A Frontier-based Approach for Autonomous Exploration,” Proc. of IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151, 1997.

    Google Scholar 

  9. Keidar, M. and Kaminka, G. A., “Efficient Frontier Detection for Robot Exploration,” The International Journal of Robotics Research, Vol. 33, No. 2, pp. 215–236, 2014.

    Article  Google Scholar 

  10. Al khawaldah, M., and Nuchter, A., “Enhanced Frontier-based Exploration For Indoor Environment with Multiple Robots,” Advanced Robotics, ahead-of-print, pp. 1–13, 2015.

    Google Scholar 

  11. Choi, K.-S. and Lee, S.-G., “Enhanced Slam for a Mobile Robot using Extended Kalman Filter and Neural Networks,” Int. J. Precis. Eng. Manuf., Vol. 11, No. 2, pp. 255–264, 2010.

    Article  Google Scholar 

  12. Makarenko, A., Williams, S. B., Bourgault, F., and Durrant-Whyte, H. F., “An Experiment in Integrated Exploration,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, pp. 534–539, 2002.

    Article  Google Scholar 

  13. Stachniss, C., Grisetti, G., and Burgard, W., “Information Gain-based Exploration using Rao-Blackwellized Particle Filters,” Proc. of Robotics: Science and Systems, Vol. 2, pp. 65–72, 2005.

    Google Scholar 

  14. Sim, R. and Roy, N., “Global A-Optimal Robot Exploration in SLAM,” Proc. of IEEE International Conference on Robotics and Automation, pp. 661–666, 2005.

    Google Scholar 

  15. Pinies, P. and Tardos, J. D., “Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision,” IEEE Transactions on Robotics, Vol. 24, No. 5, pp. 1094–1106, 2008.

    Article  Google Scholar 

  16. Estrada, C., Neira, J., and Tardos, J. D., “Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments,” IEEE Transactions on Robotics, Vol. 21, No. 4, pp. 588–596, 2005.

    Article  Google Scholar 

  17. Ahn, S. H., Choi, J., Doh, N. L., and Chung, W. K., “A Practical Approach for EKF-SLAM in an Indoor Environment: Fusing Ultrasonic Sensors and Stereo Camera,” Autonomous Robots, Vol. 24, No. 3, pp. 315–335, 2008.

    Article  Google Scholar 

  18. Blanco, J.-L., Fernandez-Madrigal, J.-A., and Gonzalez, J., “A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM,” Proc. of IEEE International Conference on Robotics and Automation, pp. 2061–2067, 2007.

    Google Scholar 

  19. Vallve, J. and Andrade-Cetto, J., “Potential Information Fields for Mobile Robot Exploration,” Robotics and Autonomous Systems, Vol. 69, pp. 68–79, 2015.

    Article  Google Scholar 

  20. Valencia, R., Morta, M., Andrade-Cetto, J., and Porta, J. M., “Planning Reliable Paths with Pose Slam,” IEEE Transactions on Robotics, Vol. 29, No. 4, pp. 1050–1059, 2013.

    Article  Google Scholar 

  21. Ryu, H. and Chung, W. K., “Local Map-based Exploration for Mobile Robots,” Intelligent Service Robotics, Vol. 6, No. 4, pp. 199–209, 2013.

    Article  Google Scholar 

  22. Carpin, S., “Fast and Accurate Map Merging for Multi-Robot Systems,” Autonomous Robots, Vol. 25, No. 3, pp. 305–316, 2008.

    Article  Google Scholar 

  23. Lester, P., “A* Pathfinding for Beginners,” http://www.gamedev.net/ page/resources/_/technical/artificial-intelligence/a-pathfinding-forbeginners-r2003 (Accessed 14 JUL 2015)

  24. Lu, F. and Milios, E., “Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans,” Journal of Intelligent and Robotic Systems, Vol. 18, No. 3, pp. 249–275, 1997.

    Article  Google Scholar 

  25. Ryu, H. and Chung, W. K., “Efficient Scan Matching Method using Direction Distribution,” Electronics Letters, Vol. 51, No. 9, pp. 686–688, 2015.

    Article  Google Scholar 

  26. Besl, P. J. and McKay, N. D., “A Method for Registration of 3-D Shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 2, pp. 239–256, 1992.

    Article  Google Scholar 

  27. Martinez, J. L., Gonzalez, J., Morales, J., Mandow, A., and Garcia-Cerezo, A. J., “Mobile Robot Motion Estimation by 2D Scan Matching with Genetic and Iterative Closest Point Algorithms,” Journal of Field Robotics, Vol. 23, No. 1, pp. 21–34, 2006.

    Article  MATH  Google Scholar 

  28. Yaakov, B.-S., Li, X., and Thiagalingam, K., “Estimation with Applications to Tracking and Navigation,” New York: Johh Wiley and Sons, pp. 404–407, 2001.

    Google Scholar 

  29. Cole, D. M. and Newman, P. M., “Using Laser Range Data for 3D Slam in Outdoor Environments,” Proc. of IEEE International Conference on Robotics and Automation, pp. 1556–1563, 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyejeong Ryu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ryu, H., Chung, W.K. Local map-based exploration using a breadth-first search algorithm for mobile robots. Int. J. Precis. Eng. Manuf. 16, 2073–2080 (2015). https://doi.org/10.1007/s12541-015-0269-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-015-0269-9

Keywords

Navigation