Cyber-Physical Construction Platform

Research Project 8-2 (RP 8-2)

IntCDC Research Project 8-2
CYBER-PHYSICAL CONSTRUCTION PLATFORM

The aim of this project is to extend the cyber-physical construction platform developed in the previous Research Project RP 8-1, consisting of a tower crane with image-based sensor network, a spider crane and novel gripper systems of both platforms, to autonomously perform trustworthy geo-referenced assembly tasks in absolute coordinates. In the previous the project, the tower crane was automated to enable geo-referenced positioning in absolute coordinates for payloads with the tower crane as a stand-alone system. In addition, a gripper was developed that is able to incline attached payloads in two directions.

In order to achieve automated assembly, the next step is to realise autonomous load picking, to extend the environmental sensing capabilities to highly dynamic environments, allowing dynamic obstacles to be avoided during transport, and to investigate collaborative assembly for the tower and spider crane working together, supported by the image-based sensing system. For autonomous picking, the gripper developed in the previous project will be extended to pick up elements without human interaction. The prefabricated element to be transported is identified by image sensors. The crane then picks up the element, taking into account dynamic contact situations.

For the environmental sensing, the focus is on methods to derive an environmental model of the construction site that shows the current state in real time. This makes it possible to investigate avoidance strategies as soon as a dynamic obstacle interferes with the currently planned path. For assembly, we are extending the collaboration between the tower crane and the spider crane to assembly processes. For the tower crane, the main research topic is the rope-guided collaborative assembly, i.e. the rope-guided handling of payloads, taking into account the contact forces between the manipulator and existing building stock parts. The image-based sensor network is integrated into the assembly process by reconstructing the occluded areas covered by the transported prefabricated element and by tracking the surroundings of the payload.

The intended automated assembly subprocesses will be shaped by distributed, discontinuous and potentially fragmented interactions between different objects and human operators [1,2,3]. This type of increased ‘interobjectivity’ between objects such as the tower crane, spider crane, mobile fabrication platform and mobile robots within cyber-physical construction systems (CPCS) [1,3] raises social science questions about the controllability and trustworthiness of CPCS and the relationship between automation and accountability [4]. Therefore, we will examine the requirements for trust(worthiness) from the perspective of both the human-machine operators and the contracting organisations: What are the prerequisites for trust in automated, hybrid technology and the related socio-technical systems? Is specific knowledge required to control a cyber-physical construction platform and to avoid risk situations? Starting from three levels of trust building (human-machine interaction, organisation, socio-technical systems), we highlight their relative importance, integration and competence requirements. By examining the conditions for a responsible and socially robust work situation in CPCS [5,6], we aim to contribute to a socially sustainable and future-proof building culture.

[1] Rammert, “Technik – Handeln – Wissen. Zu einer pragmatistischen Technik- und Sozialtheorie,“ Springer VS, Wiesbaden, 2016.
[2] B. Whitworth, “A Brief Introduction to Sociotechnical Systems,” in IGI Global (eds.). Encyclopedia of Information Science and Technology, Second Edition, pp. 394–400, 2009.
[3] G. Sammut, F. Moghaddam, “Interobjectivity”, in T. Teo (ed.) Encyclopedia of Critical Psychology, Springer, New York.
[4] Saurwein, “Emerging structures of control for algorithms on the internet. Distributed agency – distributed accountability” in T. Eberwein, S. Fengler, and M. Karmasin (eds.), Media Accountability in the era of posttruth politics, Routledge, London, pp. 196–211, 2019.
[5] C. Kropp, “Hybride Kontrolle” in F. Sprenger (ed.), Autonome Autos. Medien- und kulturwissenschaftliche Perspektiven auf die Zukunft der Mobilität, transcript, Bielefeld, pp. 85–116, 2021.
[6] N. Huchler, L. Adolph,  E. André, W. Bauer, N. Bender,  N. Müller, R. Neuburger, M. Peissner, J. Steil, S. Stowasser, and O. Suchy, “Kriterien für die menschengerechte Gestaltung der Mensch-Maschine-Interaktion bei Lernenden Systemen,” Whitepaper aus der Plattform Lernende Systeme, München, 2020.

 

PRINCIPAL INVESTIGATORS

Prof. Dr.-Ing. habil. Dr. h.c. Oliver Sawodny
Institute for System Dynamics (ISYS), University of Stuttgart
Prof. Dr.-Ing. Uwe Sörgel
Institute for Photogrammetry (IFP), University of Stuttgart
Prof. Dr. Cordula Kropp
Institute for Social Sciences (SOWI), University of Stuttgart

TEAM

apl. Prof. Dr.-Ing. Norbert Haala (IFP)
Dr.-Ing. Michael Cramer (IFP)
Dr.-Ing. Johannes Schüle (ISYS)
Mark Burkhardt (ISYS)
Lena Joachim (IFP)
Philipp Arnold (ISYS)
Ann-Kathrin Wortmeier (SOWI)
Amelie Schreck (SOWI)

PEER-REVIEWED PUBLICATIONS

  1. 2024

    1. Schüle, J., Burkhardt, M., Gienger, A., & Sawodny, O. (2024). Towards Automated Construction: Visual-based Pose Reconstruction for Tower Crane Operations using Differentiable Rendering and Network-based Image Segmentation. 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE), 1–7. https://doi.org/10.1109/ISIE54533.2024.10595817

  2. 2023

    1. Burkhardt, M., Gienger, A., Joachim, L., Haala, N., Sörgel, U., & Sawodny, O. (2023). Data-based error compensation for georeferenced payload path tracking of automated tower cranes. Mechatronics, 94, 103028--. https://doi.org/10.1016/j.mechatronics.2023.103028
    2. Burkhardt, M., Gienger, A., & Sawodny, O. (2023). Optimization-Based Multipoint Trajectory Planning Along Straight Lines for Tower Cranes. IEEE Transactions on Control Systems Technology, 1–8. https://doi.org/10.1109/TCST.2023.3308762
    3. Burkhardt, M., Joachim, L., Gienger, A., Haala, N., Sörgel, U., & Sawodny, O. (2023, August). Rotation Control of a Novel Crane Gripper with Visual-Inertial Feedback. Proceedings of 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE).
    4. Collmar, D., Walter, V., Koelle, M., & Soergel, U. (2023). FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, 159--166. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-159-2023
    5. Haala, N., Zhang, W., Joachim, L., Skuddis, D., Abolhasani, S., Schwieger, V., & Soergel, U. (2023). Zum Potenzial von SLAM-Verfahren für geodätische Echtzeit-Messaufgaben. Allgemeine Vermessungsnachrichten (Avn), 163–172. https://gispoint.de/artikelarchiv/avn/2023/avn-ausgabe-052023/7879-zum-potenzial-von-slam-verfahren-fuer-geodaetische-echtzeit-messaufgaben.html
    6. Schneider, P. J., Yang, C.-H., Li, Y., Koppe, M., Soergel, U., Pakzad, K., & Rudolf, T. (2023). DEVELOPMENT OF A WEB PLATFORM TO VISUALIZE PS-INSAR DATA IN A BUILDING INFORMATION MANAGEMENT SYSTEM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023, 869--873. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-869-2023
    7. Zhang, X., Lin, D., Xue, R., & Soergel, U. (2023). TARGET-GUIDED LEARNING FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023, 1693--1698. https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1693-2023

  3. 2022

    1. Burkhardt, M., Joachim, L., Lerke, O., Thomas, M., Schwieger, V., Haala, N., & Sawodny, O. (2022). Kran. Deutsches Patent- und Markenamt.
    2. Joachim, L., Zhang, W., Haala, N., & Soergel, U. (2022). Evaluation of the quality of real-time mapping with crane cameras and visual SLAM algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2022, 545–552. https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-545-2022
    3. Thomas, M., & Sawodny, O. (2022). Flatness-based feedforward and modal model-predictive state-feedback control of a double pendulum bridge crane. The 9th IFAC Symposium on Mechatronic Systems & The 16th International Conference on Motion and Vibration Control, 24–29.
    4. Wolff, F., Uchiyama, N., Burkhardt, M., & Sawodny, O. (2022). Nonlinear Model Predictive Control with Non-Equidistant Discretization Time Grids for Rotary Cranes. 2022 13th Asian Control Conference (ASCC), 1753–1758. https://doi.org/10.23919/ASCC56756.2022.9828180

  4. 2021

    1. Burkhardt, M., & Sawodny, O. (2021). Towards Modeling and Control of a Crane-Collaboration for the Automated Assembly of Timber Structures. IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6. https://doi.org/10.1109/IECON48115.2021.9589787
    2. Burkhardt, M., & Sawodny, O. (2021). A graph-based path planning algorithm for the control of tower cranes. 2021 American Control Conference (ACC), 1736–1741. https://doi.org/10.23919/ACC50511.2021.9482797
    3. Rauscher, F., & Sawodny, O. (2021). Efficient Online Trajectory Planning for Integrator Chain Dynamics using Polynomial Elimination. IEEE Robotics and Automation Letters, 6(3), Article 3. https://doi.org/10.1109/LRA.2021.3072857
    4. Thomas, M., Werner, T., & Sawodny, O. (2021). Online trajectory generation and feedforward control for manually-driven cranes with input constraints. 2021 IEEE Conference on Control Technology and Applications (CCTA).

OTHER PUBLICATIONS

  1. 2023

    1. Burkhardt, M., Joachim, L., Gienger, A., Haala, N., Sörgel, U., & Sawodny, O. (2023). Datenbasiertes Korrektursystem für Lastpositionsfehler von automatisierten Turmdrehkranen in Absolutkoordinaten. Deutsches Patent- und Markenamt.
    2. Kölle, M., Walter, V., Shiller, I., & Soergel, U. (2023). Efficient and Accurate Tree Detection from 3D Point Clouds through Paid Crowdsourcing. https://doi.org/10.48550/arXiv.2308.14499

  2. 2022

    1. Ackermann, S., & Joachim, L. (2022). Simulation und Auswertung eines photogrammetrischen Bildverbandes aus Krankamerabildern. 42. Wissenschaftlich-Technische Jahrestagung Der DGPF, 297–303. https://doi.org/10.24407/KXP:1796047422

  3. 2021

    1. Joachim, L., Ackermann, S., Haala, N., & Soergel, U. (2021). Using Crane Cameras for Workspace Mapping and Monitoring for an Autonomous Tower Crane. Proceedings of the 7th International Conference on Machine Control & Guidance, 47–53. http://mobilearbeitsmaschine.de/downloads.html?file=files/MCG2021/MCG2021_Conference%20Proceedings_Web.pdf
To the top of the page