Cloud-Based System Architecture for Driver Assistance in Mobile Machinery

O. Koch
Technische Universität Dresden, Dresden, Germany

B. Beck
Technische Universität Dresden, Dresden, Germany

G. Heß
Technische Universität Dresden, Dresden, Germany

C. Richter
Technische Universität Dresden, Dresden, Germany

V. Waurich
Technische Universität Dresden, Dresden, Germany

J. Weber
Technische Universität Dresden, Dresden, Germany

C. Werner
Technische Universität Dresden, Dresden, Germany

U. Aßmann
Technische Universität Dresden, Dresden, Germany

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp1714481

Ingår i: Proceedings of 15:th Scandinavian International Conference on Fluid Power, June 7-9, 2017, Linköping, Sweden

Linköping Electronic Conference Proceedings 144:8, s. 81-90

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Publicerad: 2017-12-20

ISBN: 978-91-7685-369-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Using the example of a wheel loader, this paper presents a cloud-based system architecture enabling intelligent machine behavior. In order to achieve the final goal of a fully automated bucket filling routine, while controlling the loaders engine, travel drive and attachment, different levels of automation are processed gradually. As a first step towards automation, driver assistance can be considered. The paper explains the design choices for a cyber-physicalsystem architecture in the context of construction machinery. This comprises the communication framework and the cloud-application for self-adapting systems (i.e. the MAPE-K loop). As a validation of the architecture and as a demonstrator, a driver assistance functionality has been implemented. Calculations from the cloud-application give the operator feedback about efficiency, loads and task status. A developed visualization app on a tablet serves as user-interface. Concurrent simulation allow an optimization of control algorithms for the machine control and the trajectory planning. Besides changing the parametrization of the underlying models, a solution to change ECU-code at run-time without interrupting the operation is presented. The developed system architecture is the basis for further implementations of adaptive algorithms that improve future machine operation.


Cloud computing, smart metering, IIoT, construction machinery, systems architecture


[1] R. Parasuraman, T. B. Sheridan, and C. D. Wickens. A model for types and levels of human interaction with automation. Trans. Sys. Man Cyber. Part A, 30(3):286–297, May 2000.

[2] Sae j3016 - taxonomy and definitions for terms related to on-road motor vehicle automated driving systems.

[3] Martin Laube and Steffen Haack. Condition monitoring for hydraulic power units–user-oriented entry in industry 4.0. In 10th International Fluid Power Conference (10. IFK) March 8 - 10, 2016 in Dresden, volume 2, pages 393–402. Dresdner Verein zur Förderung der Fluidtechnik e.V.

[4] Reno Filla. Evaluating the efficiency of wheel loader bucket designs and bucket filling strategies with noncoupled dem simulations and simple performance indicators. pages 274–292, Dresden.

[5] Reno Filla. A study to compare trajectory generation algorithms for automatic bucket filling in wheel loaders. [6] S. Dadhich, U. Bodin, and U. Andersson. Key challenges in automation of earth-moving machines. 68:212–222, 2016.

[7] Elisabet Altin and Brian O’Sullivan. Volvo construction equipment reveals prototype autonomous machines, 2016.

[8] Jonatan Björkman. Control of an autonomous wheel loader.

[9] Anders Bergdahl. Autonomous bucket emptying on hauler, 2011.

[10] H. Derhamy, J. Eliasson, J. Delsing, and P. Priller. A survey of commercial frameworks for the internet of things. In 2015 IEEE 20th Conference on Emerging Technologies Factory Automation (ETFA), pages 1–8, Sept 2015.

[11] Autonomic Computing et al. An architectural blueprint for autonomic computing. IBM White Paper, 31, 2006.

[12] Angel Diaz and Chris Ferris. Ibm’s open cloud architecture. IBM Corp., Armonk, New York, 2013.

[13] OASIS Standard. Mqtt version 3.1.1, 2014.

[14] Yuriy Brun, Giovanna Di Marzo Serugendo, Cristina Gacek, Holger Giese, Holger Kienle, Marin Litoiu, Hausi Müller, Mauro Pezzè, and Mary Shaw. Engineering Self-Adaptive Systems through Feedback Loops, pages 48–70. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.

[15] Vasileios Karagiannis, Periklis Chatzimisios, Francisco Vazquez-Gallego, and Jesus Alonso-Zarate. A survey on application layer protocols for the internet of things. Transaction on IoT and Cloud Computing, 3(1):11–17, 2015.

[16] Roger Light. Eclipse mosquitto, 2010. An Open Source MQTT v3.1/v3.1.1 Broker.

[17] Fredrik Gustafsson. Slip-based tire-road friction estimation. 33(6):1087–1099, 1997.

[18] Heinrich Schneider and Peter Reitz. GPS zur geschwindigkeitsmessung. 51(5):264–265, 1996.

[19] LUXACT - optical sensor for non-contact displacement and speed measurement, 2013.

[20] Correvit s-motion - berührungslose optische sensoren, 2016.

[21] Chris C. Ward and Karl Iagnemma. A dynamic-modelbased wheel slip detector for mobile robots on outdoor terrain. 24(4):821–831, 2008.

[22] ModelicaR - a unified object-oriented language for systems modeling language specification version 3.3, 2012.

[23] Torsten Blochwitz, Martin Otter, Johan Akesson, Martin Arnold, Christoph Clauss, Hilding Elmqvist, Markus Friedrich, Andreas Junghanns, Jakob Mauss, Dietmar Neumerkel, et al. Functional mockup interface 2.0: The standard for tool independent exchange of simulation models. In Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany, number 076, pages 173–184. Linköping University Electronic Press, 2012.

[24] Peter A Cundall. A computer model for simulating progressive large scale movements in blocky rock systems. In Proceedings Symposium Int. Soc. Rock Mech (ISRM), volume 1, pages 8–11, Nancy Metz, 1971.

[25] Christoph Kloss and Christoph Goniva. Liggghts–open source discrete element simulations of granular materials based on lammps. Supplemental Proceedings: Materials Fabrication, Properties, Characterization, and Modeling, Volume 2, pages 781–788, 2011.

[26] Christian Richter. A new approach for integrating discrete element method into component-oriented system simulations. In ASIM 2016 - 23. Symposium Simulationstechnik 07.-09.09.2016. Zusammenfassung der Beiträge, pages 91–97, HTW Dresden, 2016.

[27] Günther Kunze, Andre Katterfeld, Christian Richter, Hendrik Otto, and Christian Schubert. Plattform- und softwareunabhängige simulation der erdstoff-maschine interaktion. In 5. Fachtagung Baumaschinentechnik, Dresden, 2012.

[28] Tobias Bellmann. Interactive simulations and advanced visualization with modelica. In Proceedings of the 7th International Modelica Conference; Como; Italy; 20-22 September 2009, number 043, pages 541–550. Linköping University Electronic Press, 2009.

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