Abstract
Assume a robot operating in a public space (e.g., a library, a museum) and serving visitors as a companion, a guide or an information stand. To do that, the robot has to interact with humans, which presumes that it actively searches for humans in order to interact with them. This paper addresses the problem how to plan robot’s actions in order to maximize the number of such interactions in the case human behavior is not known in advance. We formulate this problem as the exploration/exploitation problem and design several strategies for the robot. The main contribution of the paper than lies in evaluation and comparison of the designed strategies on two datasets. The evaluation shows interesting properties of the strategies, which are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Exploration, exploitation, mixture or artificial utility can be used as the utility in a particular node.
- 2.
The original dataset [4] contains one year-long collection of measurements from 50 different sensors spread over the apartment and we filtered this data to contain information about presence of the person in particular rooms and at particular times.
- 3.
In fact, the person was not present in the flat occasionally or was visited by another people.
References
Amigoni, F., Caglioti, V.: An information-based exploration strategy for environment mapping with mobile robots. Robot. Auton. Syst. 58(5), 684–699 (2010)
Basilico, N., Amigoni, F.: Exploration strategies based on multi-criteria decision making for an autonomous mobile robot. In: Proceedings of 4th European Conference on Mobile Robots, pp. 259–264. KoREMA (2009)
Basilico, N., Amigoni, F.: Exploration strategies based on multi-criteria decision making for searching environments in rescue operations. Auton. Robot. 31(4), 401–417 (2011)
Cook, D.J.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 2010(99), 1 (2010)
Gerling, K., Hebesberger, D., Dondrup, C., Körtner, T., Hanheide, M.: Robot deployment in long-term care. Zeitschrift für Gerontologie und Geriatrie 1–9 (2016). http://dx.doi.org/10.1007/s00391-016-1065-6
Gonzalez-Banos, H.H., Latombe, J.C.: Navigation strategies for exploring indoor environments. Int. J. Robot. Res. 21(10–11), 829–848 (2002)
Hebesberger, D., Dondrup, C., Koertner, T., Gisinger, C., Pripfl, J.: Lessons learned from the deployment of a long-term autonomous robot as companion in physical therapy for older adults with dementia: a mixed methods study. In: 11th ACM/IEEE International Conference on Human Robot Interaction, HRI 2016, pp. 27–34. IEEE Press, Piscataway (2016). http://dl.acm.org/citation.cfm?id=2906831.2906838
Hollinger, G., Djugash, J., Singh, S.: Coordinated search in cluttered environments using range from multiple robots. In: Laugier, C., Siegwart, R. (eds.) Field and Service Robotics. STAR, vol. 42, pp. 433–442. Springer, Berlin Heidelberg (2008)
Koenig, S., Tovey, C., Halliburton, W.: Greedy mapping of terrain. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 4, pp. 3594–3599 (2001)
Koutsoupias, E., Papadimitriou, C., Yannakakis, M.: Searching a fixed graph. In: Meyer auf der Heide, F., Monien, B. (eds.) ICALP 1996. LNCS, vol. 1099, pp. 280–289. Springer, Heidelberg (1996). doi:10.1007/3-540-61440-0_135
Krajník, T., Santos, J.M., Duckett, T.: Life-long spatio-temporal exploration of dynamic environments. In: 2015 European Conference on Mobile Robots (ECMR), pp. 1–8, September 2015
Krajník, T., Fentanes, J.P., Cielniak, G., Dondrup, C., Duckett, T.: Spectral analysis for long-term robotic mapping. In: 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014)
Krajník, T., Fentanes, J.P., Mozos, O.M., Duckett, T., Ekekrantz, J., Hanheide, M.: Long-term topological localization for service robots in dynamic environments using spectral maps. In: International Conference on Intelligent Robots and Systems (IROS) (2014)
Krajník, T., Kulich, M., Mudrová, L., Ambrus, R., Duckett, T.: Where’s Waldo at time t? Using spatio-temporal models for mobile robot search. In: 2014 IEEE International Conference on Robotics and Automation (ICRA) (2015)
Kulich, M., Faigl, J., Přeučil, L.: On distance utility in the exploration task. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4455–4460, May 2011
Kulich, M., Přeučil, L., Miranda Bront, J.: Single robot search for a stationary object in an unknown environment. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5830–5835, May 2014
Kulich, M., Miranda-Bront, J.J., Přeučil, L.: A meta-heuristic based goal-selection strategy for mobile robot search in an unknown environment. Comput. Oper. Res. (2016). ISSN 0305-0548, http://dx.doi.org/10.1016/j.cor.2016.04.029
Makarenko, A.A., Williams, S.B., Bourgault, F., Durrant-Whyte, H.F.: An experiment in integrated exploration. In: IEEE/RSJ International Conference on Intelligent Robots and System, pp. 534–539. IEEE (2002)
Santos, J.M., Krajnik, T., Pulido Fentanes, J., Duckett, T.: Lifelong information-driven exploration to complete and refine 4D spatio-temporal maps. Robot. Autom. Lett. 1, 684–691 (2016)
Sarmiento, A., Murrieta-Cid, R., Hutchinson, S.: A multi-robot strategy for rapidly searching a polygonal environment. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 484–493. Springer, Heidelberg (2004)
Tovar, B., Muñoz-Gómez, L., Murrieta-Cid, R., Alencastre-Miranda, M., Monroy, R., Hutchinson, S.: Planning exploration strategies for simultaneous localization and mapping. Robot. Auton. Syst. 54(4), 314–331 (2006)
Tovey, C., Koenig, S.: Improved analysis of greedy mapping. In: 2003 Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2003), vols. 3 and 4, pp. 3251–3257, October 2003
Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151. IEEE Computer Society Press (1997)
Acknowledgments
This work has been supported by the Technology Agency of the Czech Republic under the project no. TE01020197 “Centre for Applied Cybernetics” and by the EU ICT project 600623 ‘STRANDS’.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Kulich, M., Krajník, T., Přeučil, L., Duckett, T. (2016). To Explore or to Exploit? Learning Humans’ Behaviour to Maximize Interactions with Them. In: Hodicky, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2016. Lecture Notes in Computer Science(), vol 9991. Springer, Cham. https://doi.org/10.1007/978-3-319-47605-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-47605-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47604-9
Online ISBN: 978-3-319-47605-6
eBook Packages: Computer ScienceComputer Science (R0)