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System-controlled user interaction within the service robotic control architecture MASSiVE

Published online by Cambridge University Press:  01 March 2007

Oliver Prenzel ,
Christian Martens ,
Marco Cyriacks ,
Chao Wang  and
Axel Gräser
Oliver Prenzel*
Affiliation:
Institute of Automation, University of Bremen, Bremen, Germany
Christian Martens
Affiliation:
Rheinmetall Defence Electronics GmbH, LTMR, Bremen, Germany
Marco Cyriacks
Affiliation:
Institute of Automation, University of Bremen, Bremen, Germany
Chao Wang
Affiliation:
Institute of Automation, University of Bremen, Bremen, Germany
Axel Gräser
Affiliation:
Institute of Automation, University of Bremen, Bremen, Germany
*
*Corresponding author. Email: [email protected]
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Summary

This paper presents an approach to reduce the technical complexity of a service robotic system by means of systematic and well-balanced user-involvement. By taking advantage of the user's cognitive capabilities during task execution, a technically manageable robotic system, which is able to execute tasks on a high level of abstraction reliably and robustly, emerges. For the realisation of this approach, the control architecture MASSiVE has been implemented, which is used for the control of the rehabilitation robot FRIEND II. It supports task execution on the basis of a priori defined and formally verified task-knowledge. This task-knowledge contains all possible sequences of operations as well as the symbolic representation of objects required for the execution of a specific task. The seamless integration of user interactions into this task-knowledge, in combination with MASSiVE's user-adapted human–machine interface layer, enables the system to deliberately interact with the user during run-time.

Keywords

Semi-autonomyControl architectureService robotVerified task knowledgeControlled human machine interactionHuman machine interface
Type
Article
Information
Robotica , Volume 25 , Issue 2 , March 2007 , pp. 237 - 244
Copyright
Copyright © Cambridge University Press 2007

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References

1.Engelberger, J. F., Robotics in Service, 1st ed. (MIT Press, Cambridge, MA, 1989) ISBN 0-2620-5042-0.CrossRefGoogle Scholar
2.Dario, P., Dillman, R. and Christensen, H. I., “Euron Research Roadmaps.” Key Area 1 on “Research Coordination.” Available: http://www.euron.org (2004)Google Scholar
3.Laschi, C., Teti, G., Tamburrini, G., Datteri, E. and Dario, P., “Adaptable Semi-Autonomy in Personal Robots,” Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, Bordeaux, Paris, France (2001) vol. 10, pp. 152–157.Google Scholar
4.Martens, C., Kim, D.-J., Han, J.-S., Gräser, A. and Bien, Z. Z., “Concept for a Modified Hybrid Multi-Layer Control-Architecture for Rehabilitation Robots,” Proceedings of the 3rd International Workshop on Human-Friendly Robotic Systems, (Jan. 21–22, (2002), pp. 49–54.Google Scholar
5.Colle, E., Rybawzyk, Y. and Hoppenot, P., “Arph: An Assistant Robot for Disabled People,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Hammanet, Tunisia (2002) vol. 1, pp. 176–181. ISBN 0-7803-7437-1.CrossRefGoogle Scholar
6.Amazon.com, “Amazon Mechanical Turk—Artificial, Artificial Intelligence.” URL. Available: http://www.mturk.com (2006).Google Scholar
7.Hans, M., Eine Modulare Kontrollarchitektur für den Hol- und Bringdienst von Roboterassistenten Ph.D. Thesis (Germany, Stuttgart: University of Stuttgart, (2005).Google Scholar
8.Nesnas, I. A., Simmons, R., Gaines, D., Kunz, C., Diaz-Calderon, A., Estlin, T., Madison, R., Guineau, J., McHenry, M., Shu, I.-H. and Apfelbaum, D., “Challenges and steps toward reusable robotic software,” Int. J. Adv. Robot. Sys. (2006). 3, 23–30 (2006), ISSN 17298806.Google Scholar
9.Martens, C., Prenzel, O., Feuser, J. and Gräser, A., “MASSiVE: Multi-Layer Architecture for Semi-Autonomous Service-Robots with Verified Task Execution,” Proceedings of the 10th International Conference on Optimization of Electrical and Electronic Equipments, Brasov, Romania (2006) vol. 3, pp. 107–112, ISBN 9-7365-3705-8.Google Scholar
10.Ivlev, O., Martens, C. and Graeser, A., “Rehabilitation Robots FRIEND-I and FRIEND-II with the Dexterous Lightweight Manipulator,” Proceedings of the 3rd International Congress: Restoration of (Wheeled) Mobility in SCI the Rehabilitation, Amsterdam, The Netherlands (Apr. 19–21, (2005), vol. 5, pp. 111–123.Google Scholar
11.Bonasso, R., Kortenkamp, D., Schreckenghost, D. and Ryan, D., “Three Tier Architecture for Controlling Space Life Support Systems,” Proceedings of the IEEE SIS'98, Rockville, MD, USA (May 21–23, (1998), pp. 195–201.Google Scholar
12.Coste-Maniere, E. and Simmons, R., “Architecture, the Backbone of Robotic Systems,” Proceedings of the International Conference on Robotics and Automation, San Francisco, CA, USA (Apr. (2000), vol. 1, pp. 67–72. ISBN 0-7803-5886-4.Google Scholar
13.Martens, C. and Gräser, A., “Design and Implementation of a Discrete Event Controller for High-Level Command Control of Rehabilitation Robotic Systems,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (Sep. 30–Oct. 4, (2002), pp. 1457–1462.Google Scholar
14.Martens, C., “Task oriented programming of service-robots on the basis of process-structures,” Methods Appl. Autom., 45–56 (2005), ISBN 3-8322-4502-2.Google Scholar
15.Cao, T. and Sanderson, A. C., “AND/OR net representation for robotic task sequence planning,” IEEE Trans. Syst. Man Cybern. 28, 204–218 (1998).CrossRefGoogle Scholar
16.Fikes, R. and Nilsson, N., “STRIPS: A new approach to the application of theorem proving to problem solving,” Artif. Intell. 2, 189–208 (1971).CrossRefGoogle Scholar
17.Prenzel, O., “Semi-autonomous object anchoring for service-robots,” Methods Appl. Autom., 57–68 (2005), ISBN 3-8322-4502-2.Google Scholar
18.Martens, C., Teilautonome Aufgabenbearbeitung bei Rehabilitationsrobotern mit Manipulator—Konzeption und Realisierung eines Softwaretechnischen und Algorithmischen Rahmenwerks Ph.D. Thesis, Bremen, Germany (University of Bremen, (2004). Shaker Verlag.Google Scholar
19.Zucker, S. W., “Region Growing: Childhood and Adolescence,” Computer Graphics and Image Processing 5 (3)382–399, (1976).CrossRefGoogle Scholar
20.Martens, C., “Generation of parallel executable control sequences for rehabilitation robotic systems on the basis of hierachical petri-net based task representation,” Methods Appl. Autom., 73–85 (2003).Google Scholar
21.Ojdanic, D., Ivlev, O. and Gräser, A., “A new fast motion planning approach for dexterous manipulators in 3D-Cartesian space,” Proceedings of the ISR-Robotik Joint Conference on Robotics, Munich, Germany (May 15–17, (2006) p. 49.Google Scholar
22.Dey, A. K. and Abowd, G. D., Towards a Better Understanding of Context and Context-Awareness (Georgia Institute of Technology, 2000). The Hague, The Netherlands.Google Scholar
23.Gamma, E., Helm, R., Johnson, R. and Vlissides, J., Design Patterns: Elements of Reusable Object-Oriented Software 1st ed. (Addison-Wesley, Reading, MA, 1995). ISBN 0-2016-3361-2.Google Scholar