Abstract
Some applications require autonomous robots to search an initially unknown environment for static _targets, without any a priori information about environment structure and _target locations. _targets can be human victims in search and rescue or materials in foraging. In these scenarios, the environment is incrementally discovered by the robots exploiting exploration strategies to move around in an autonomous and effective way. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations on the frontier between the known and the unknown portions of the environment according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using a more theoretically-grounded approach, based on Multi-Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented some MCDM-based exploration strategies within an existing robot controller and we evaluated their performance in a simulated environment.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Amigoni, F., & Caglioti, V. (2010). An information-based exploration strategy for environment mapping with mobile robots. Robotics and Autonomous Systems, 5(58), 684–699.
Amigoni, F., & Gallo, A. (2005). A multi-objective exploration strategy for mobile robots. In Proc. IEEE international conference on robotics and automation (ICRA) (pp. 3861–3866).
Balaguer, B., Balakirsky, S., Carpin, S., & Visser, A. (2009). Evaluating maps produced by urban search and rescue robots: lessons learned from RoboCup. Autonomous Robots, 27(4), 449–464.
Basilico, N., & Amigoni, F. (2009). Exploration strategies based on multi-criteria decision making for an autonomous mobile robot. In Proc. European conference on mobile robotics (ECMR) (pp. 259–264).
Burgard, W., Moors, M., & Schneider, F. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–378.
Calisi, D., Farinelli, A., Iocchi, L., & Nardi, D. (2007). Multi-objective exploration and search for autonomous rescue robots. Journal of Field Robotics, 24(8–9), 763–777.
Calisi, D., Iocchi, L., Nardi, D., Scalzo, C., & Ziparo, V. (2008). Contextual navigation and mapping for rescue robots. In Proc. IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 19–24).
Carpin, S., Lewis, M., Wang, J., Balakirsky, S., & Scrapper, C. (2007). USARSim: a robot simulator for research and education. In Proc. IEEE international conference on robotics and automation (ICRA) (pp. 1400–1405).
Choset, H. (2001). Coverage for robotics: a survey of recent results. Annals of Mathematics and Artificial Intelligence, 31(1–4), 113–126.
de Hoog, J., Cameron, S., & Visser, A. (2009). Role-based autonomous multi-robot exploration. In Proc. computation world: future computing, service computation, cognitive, adaptive, content, patterns (pp. 482–487).
Gerkey, B., & Mataric, M. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23, 939–954.
Gonzáles-Baños, H., & Latombe, J. C. (2002). Navigation strategies for exploring indoor environments. The International Journal of Robotics Research, 21(10–11), 829–848.
Grabisch, M., & Labreuche, C. (2008). A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid. 4OR—A Quarterly Journal of Operations Research, 6(1), 1–44.
Grabisch, M., Kojadinovic, I., & Meyer, P. (2008) A review of capacity identification methods for Choquet integral based multi-attribute utility theory—applications of the Kappalab R package. European Journal of Operational Research, 186(1), 766–785.
Hollinger, G., & Singh, S. (2010). Multi-robot coordination with periodic connectivity. In Proc. IEEE international conference on robotics and automation (ICRA) (pp. 4457–4462).
Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1), 90–98.
Kleiner, A., Prediger, J., & Nebel, B. (2006). RFID technology-based exploration and SLAM for search and rescue. In Proc. IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4054–4059).
Lin, L., & Goodrich, M. (2009). UAV intelligent path planning for wilderness search and rescue. In Proc. IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 709–714).
Low, K., Dolan, J., & Khosla, P. (2008). Adaptive multi-robot wide-area exploration and mapping. In Proc. international conference on autonomous agents and multiagent systems (AAMAS) (pp. 23–30).
Marjovi, A., Nunes, J., Marques, L., & de Almeida, A. (2009). Multi-robot exploration and fire searching. In Proc. IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1929–1934).
Nevatia, Y., Stoyanov, T., Rathnam, R., Pfingsthorn, M., Markov, S., Ambrus, R., & Birk, A. (2008). Augmented autonomy: improving human-robot team performance in urban search and rescue. In Proc. IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2103–2108).
Pestman, W. (1998). Mathematical statistics: an introduction. Berlin: de Gruyter.
Rasche, C., Stern, C., Richert, W., Kleinjohann, L., & Kleinjohann, B. (2010). Combining autonomous exploration, goal-oriented coordination and task allocation in multi-UAV scenarios. In Proc. international conference on autonomic and autonomous systems (pp. 52–57).
Saeedi, P., Sorensen, S. A., & Hailes, S. (2009). Performance-aware exploration algorithm for search and rescue robots. In Proc. IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1–6).
Scone, S., & Phillips, I. (2010). Trade-off between exploration and reporting victim locations in USAR. In Proc. IEEE international symposium on a world of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–6).
Singh, A., Krause, A., Guestrin, C., & Kaiser, W. J. (2009). Efficient informative sensing using multiple robots. The Journal of Artificial Intelligence Research, 34(1), 707–755.
Stachniss, C., & Burgard, W. (2003). Exploring unknown environments with mobile robots using coverage maps. In Proc. international joint conferences on artificial intelligence (IJCAI) (pp. 1127–1134).
Tadokoro, S. (2010). Rescue robotics. Berlin: Springer.
Thrun, S. (2002). Robotic mapping: a survey. In Exploring artificial intelligence in the New Millennium (pp. 1–35).
Tovar, B., Munoz, L., Murrieta-Cid, R., Alencastre, M., Monroy, R., & Hutchinson, S. (2006). Planning exploration strategies for simultaneous localization and mapping. Robotics and Autonomous Systems, 54(4), 314–331.
Visser, A., & Slamet, B. (2008). Including communication success in the estimation of information gain for multi-robot exploration. In Proc. international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOPT) (pp. 680–687).
Visser, A., de Buy Wenniger, G., Nijhuis, H., Alnajar, F., Huijten, B., van der Velden, M., Josemans, W., Terwijn, B., Sobolewski, R., Flynn, H., & de Hoog, J. (2009). Amsterdam Oxford joint rescue forces—team description paper—RoboCup 2009. In Proc. RoboCup symposium.
Wirth, S., & Pellenz, J. (2007). Exploration transform: a stable exploring algorithm for robots in rescue environments. In Proc. IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1–5).
Yamauchi, B. (1997). A frontier-based approach for autonomous exploration. In Proc. IEEE international symposium on computational intelligence in robotics and automation (CIRA) (pp. 146–151).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Basilico, N., Amigoni, F. Exploration strategies based on multi-criteria decision making for searching environments in rescue operations. Auton Robot 31, 401–417 (2011). https://doi.org/10.1007/s10514-011-9249-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-011-9249-9