Abstract
We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the Twenty-first International Conference on Machine Learning (ICML). ACM (2004)
Arras, K.O., Grzonka, S., Luber, M., Burgard, W.: Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2008)
Biber, P., Straßer, W.: The normal distributions transform: a new approach to laser scan matching. In: IROS, pp. 2743–2748. IEEE (2003)
Burgard, W., Cremers, A., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S.: Experiences with an interactive museum tour-guide robot. Artif. Intell. 114(1–2), 3–55 (2000)
Cox, I., Hingorani, S.: An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 18(2), 138–150 (1996)
Cristani, M., Pesarin, A., Vinciarelli, A., Crocco, M., Murino, V.: Look at who’s talking: voice activity detection by automated gesture analysis. In: Constructing Ambient Intelligence, pp. 72–80. Springer (2012)
Fiore, M., Clodic, A., Alami, R.: On planning and task achievement modalities for human-robot collaboration. In: The International Symposium on Experimental Robotics (2014)
Hall, E.T.: The Hidden Dimension. Anchor Books, New York (1966)
Hung, H., Huang, Y., Friedland, G., Gatica-Perez, D.: Estimating dominance in multi-party meetings using speaker diarization. Tr. Audio Speech Lang. Process. 19(4), 847–860 (2011)
Jafari, O.H., Mitzel, D., Leibe, B.: Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras. In: International Conference on Robotics and Automation (ICRA) (2014)
Joosse, M., Poppe, R., Lohse, M., Evers, V.: Cultural differences in how an engagement-seeking robot should approach a group of people. In: Proceedings of International Conference on Collaboration Across Boundaries: Culture, Distance and Technology (CABS) (2014)
Joosse, M.P., Lohse, M., Evers, V.: How a guide robot should behave at an airport insights based on observing passengers. Technical Report TR-CTIT-15-01, Centre for Telematics and Information Technology, University of Twente, Enschede (2015)
Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. In: Proceedings of Robotics: Science and Systems (RSS) (2010)
Kruse, T., Khambhaita, H., Alami, R., Kirsch, A.: Evaluating directional cost models in navigation. In: ACM/IEEE International Conference on Human-Robot Interaction (HRI) (2014)
Kucner, T., Saarinen, J., Magnusson, M., Lilienthal, A.J.: Conditional transition maps: learning motion patterns in dynamic environments. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1196–1201 (2013)
Leibe, B., Schindler, K., Van Gool, L.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. PAMI 30(10) (2008)
Levine, S, Popovic, Z., Koltun, V.: Nonlinear inverse reinforcement learning with Gaussian processes. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 19–27 (2011)
Linder, T., Arras, K.: Multi-model hypothesis tracking of groups of people in RGB-D data. In: Proceedings of IEEE International Conference on Information Fusion (FUSION), pp. 1–7 (2014)
Luber, M., Arras, K.O.: Multi-hypothesis social grouping and tracking for mobile robots. In: Robotics: Science and Systems (RSS’13), Berlin, Germany (2013)
Magnusson, M., Lilienthal, A., Duckett, T.: Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robot. 24(10), 803–827 (2007)
Michini, B., How, J.P.: Improving the efficiency of Bayesian inverse reinforcement learning. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), St. Paul, Minnesota, USA (2012)
Mitzel, D., Leibe, B.: Close-range human detection for head-mounted cameras. In: British Machine Vision Conference (2012)
Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 116–121 (1985)
Mund, D., Triebel, R., Cremers, D.: Active online confidence boosting for efficient object classification. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2015)
Murty, K.G.: An algorithm for ranking all the assignments in order of increasing cost. Oper. Res. 16 (1968)
Ong, S.C., Png, S.W., Hsu, D., Lee, W.S.: POMDPs for robotic tasks with mixed observability. In: Proceedings of Robotics: Science and Systems (RSS) (2009)
Palmieri, L., Arras, K.: POSQ: a new RRT extend function for efficient and smooth mobile robot motion planning. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2014)
Pandey, G., McBride, J.R., Eustice, R.M.: Ford campus vision and lidar data set. Int. J. Robot. Res. 30(13), 1543–1552 (2011)
Pomerleau, F., Krüsi, P., Colas, F., Furgale, P., Siegwart, R.: Long-term 3D map maintenance in dynamic environments. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)
Saarinen, J., Andreasson, H., Stoyanov, T., Lilienthal, A.: 3D normal distributions transform occupancy maps: an efficient representation for mapping in dynamic environments. Int. J. Robot. Res. (IJRR) 1627–1644 (2013)
Siegwart, R., Arras, K.O., Bouabdallah, S., Burnier, D., Froidevaux, G., Greppin, X., Jensen, B., Lorotte, A., Mayor, L., Meisser, M., Philippsen, R., Piguet, R., Ramel, G., Terrien, G., Tomatis, N.: Robox at Expo. 02: a large-scale installation of personal robots. RAS 42(3–4), 203–222 (2003)
Stoyanov, T., Saarinen, J., Andreasson, H., Lilienthal, A.: Normal distributions transform occupancy map fusion: simultaneous mapping and tracking in large scale dynamic environments. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4702–4708 (2013)
Sudowe, P., Leibe, B.: Efficient use of geometric constraints for sliding-window object detection in video. In: International Conference on Computer Vision Systems (ICVS) (2011)
Tosato, D., Spera, M., Cristani, M., Vittorio, M.: Characterizing humans on Riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. (2013)
Triebel, R., Grimmett, H., Paul, R., Posner, I.: Driven learning for driving: how introspection improves semantic mapping. In: Proceedings of International Symposium on Robotics Research (ISRR) (2013)
Triebel, R., Stühmer, J., Souiai, M., Cremers, D.: Active online learning for interactive segmentation using sparse Gaussian processes. In: German Conference on Pattern Recognition (GCPR) (2014)
Vasquez, D., Okal, B., Arras, K.O.: Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison. In: IROS, Chicago, USA (2014)
Veenstra, A., Hung, H.: Do they like me? Using video cues to predict desires during speed-dates. In: Internatioanl Conference on Computer Vision, Workshops, pp. 838–845 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Triebel, R. et al. (2016). SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports. In: Wettergreen, D., Barfoot, T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-27702-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27700-4
Online ISBN: 978-3-319-27702-8
eBook Packages: EngineeringEngineering (R0)