AAMAS 2011: Advanced Agent Technology pp 281-294 | Cite as
Fast Frontier Detection for Robot Exploration
Conference paper
Abstract
Frontier-based exploration is the most common approach to exploration, a fundamental problem in robotics. In frontier-based exploration, robots explore by repeatedly computing (and moving towards) frontiers, the segments which separate the known regions from those unknown. However, most frontier detection algorithms process the entire map data. This can be a time consuming process which slows down the exploration. In this paper, we present two novel frontier detection algorithms: WFD, a graph search based algorithm and FFD, which is based on processing only the new laser readings data. In contrast to state-of-the-art methods, both algorithms do not process the entire map data. We implemented both algorithms and showed that both are faster than a state-of-the-art frontier detector implementation (by several orders of magnitude).
Keywords
Combinatorial Auction Graph Search Unknown Region Exploration Problem Frontier Point
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.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Apostolopoulos, D., Pedersen, L., Shamah, B., Shillcutt, K., Wagner, M., Whittaker, W.: Robotic antarctic meteorite search: Outcomes. In: IEEE International Conference on Robotics and Automation, pp. 4174–4179 (2001)Google Scholar
- 2.Berhault, M., Huang, H., Keskinocak, P., Koenig, S., Elmaghraby, W., Griffin, P., Kleywegt, A.: Robot exploration with combinatorial auctions. In: Proceedings of the International Conference on Intelligent Robots and Systems, pp. 1957–1962 (2003)Google Scholar
- 3.Bouraqadi, N., Doniec, A., de Douai, E.M.: Flocking-Based Multi-Robot Exploration. In: National Conference on Control Architectures of Robots (2009)Google Scholar
- 4.Bresenham, J.: Algorithm for computer control of a digital plotter. IBM Systems Journal 4(1), 25–30 (2010)CrossRefGoogle Scholar
- 5.Burgard, W., Moors, M., Fox, D., Simmons, R., Thrun, S.: Collaborative multi-robot exploration. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 476–481 (2000)Google Scholar
- 6.Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated multi-robot exploration. IEEE Transactions on Robotics 21(3), 376–378 (2005)CrossRefGoogle Scholar
- 7.Calisi, D., Farinelli, A., Iocchi, L., Nardi, D.: Multi-objective exploration and search for autonomous rescue robots: Research articles. J. Field Robot. 24, 763–777 (2007)CrossRefGoogle Scholar
- 8.Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press (2001)Google Scholar
- 9.Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2443–2448 (2005)Google Scholar
- 10.Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Transactions on Robotics 23, 34–46 (2007)CrossRefGoogle Scholar
- 11.Hougen, D.F., Benjaafar, S., Bonney, J., Budenske, J., Dvorak, M., Gini, M.L., French, H., Krantz, D.G., Li, P.Y., Malver, F., Nelson, B.J., Papanikolopoulos, N., Rybski, P.E., Stoeter, S., Voyles, R.M., Yesin, K.B.: A miniature robotic system for reconnaissance and surveillance. In: ICRA, pp. 501–507 (2000)Google Scholar
- 12.Howard, A., Roy, N.: The robotics data set repository, RADISH (2003), http://radish.sourceforge.net/
- 13.Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., Shimada, S.: Robocup rescue: Search and rescue in large-scale disasters as a domain for autonomous agents research. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 739–746. IEEE Computer Society (1999)Google Scholar
- 14.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 International Conference on Intelligent Robots and Systems (IROS), pp. 3232–3238 (2003)Google Scholar
- 15.Lau, H.: NSW, A.: Behavioural approach for multi-robot exploration. In: Australasian Conference on Robotics and Automation (ACRA), Brisbane (December 2003)Google Scholar
- 16.Sawhney, R., Krishna, K.M., Srinathan, K.: On fast exploration in 2D and 3D terrains with multiple robots. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 73–80 (2009)Google Scholar
- 17.Stachniss, C.: Exploration and Mapping with Mobile Robots. Ph.D. thesis, University of Freiburg, Department of Computer Science (2006)Google Scholar
- 18.Visser, A.: personal communication. Email (January 4, 2011)Google Scholar
- 19.Visser, A., Slamet, B.A.: Including communication success in the estimation of information gain for multi-robot exploration. In: Proceedings of the 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2008), pp. 680–687. IEEE Publishing (April 2008)Google Scholar
- 20.Wurm, K.M.: Personal communication. Email (January 20, 2011)Google Scholar
- 21.Wurm, K., Stachniss, C., Burgard, W.: Coordinated multi-robot exploration using a segmentation of the environment. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France (September 2008)Google Scholar
- 22.Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151. IEEE Computer Society, Washington, DC, USA (1997)Google Scholar
- 23.Yamauchi, B.: Frontier-based exploration using multiple robots. In: Proceedings of the Second International Conference on Autonomous Agents, pp. 47–53 (1998)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2012