Towards Visual Feedback Loops for Robot-Controlled Additive Manufacturing

  • Sheila Sutjipto
  • Daniel Tish
  • Gavin Paul
  • Teresa Vidal-Calleja
  • Tim SchorkEmail author
Conference paper


Robotic additive manufacturing methods have enabled the design and fabrication of novel forms and material systems that represent an important step forward for architectural fabrication. However, a common problem in additive manufacturing is to predict and incorporate the dynamic behavior of the material that is the result of the complex confluence of forces and material properties that occur during fabrication. While there have been some approaches towards verification systems, to date most robotic additive manufacturing processes lack verification to ensure deposition accuracy. Inaccuracies, or in some instances critical errors, can occur due to robot dynamics, material self-deflection, material coiling, or timing shifts in the case of multi-material prints. This paper addresses that gap by presenting an approach that uses vision-based sensing systems to assist robotic additive manufacturing processes. Using online image analysis techniques, occupancy maps can be created and updated during the fabrication process to document the actual position of the previously deposited material. This development is an intermediary step towards closed-loop robotic control systems that combine workspace sensing capabilities with decision-making algorithms to adjust toolpaths to correct for errors or inaccuracies if necessary. The occupancy grid map provides a complete representation of the print that can be analyzed to determine various key aspects, such as, print quality, extrusion diameter, adhesion between printed parts, and intersections within the meshes. This valuable quantitative information regarding system robustness can be used to influence the system’s future actions. This approach will help ensure consistent print quality and sound tectonics in robotic additive manufacturing processes, improving on current techniques and extending the possibilities of robotic fabrication in architecture.


Robot control 3D printing Vision-based sensing 


  1. Barry, R.A., Shepherd, R.F., Hanson, J.N., Nuzzo, R.G., Wiltzius, P., Lewis, J.A.: Direct-write assembly of 3D hydrogel scaffolds for guided cell growth. Adv. Mater. 21, 2407–2410 (2009)CrossRefGoogle Scholar
  2. Bouguet, J.Y.: Matlab camera calibration toolbox. Caltech Technical report, Accessed 1 Jun 2018
  3. Daniilidis, K.: Hand-eye calibration using dual quaternions. Int. J. Robot. Res. 18(3), 286–298 (1999)CrossRefGoogle Scholar
  4. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22, 46–57 (1989)CrossRefGoogle Scholar
  5. Furrer, F., Fehr, M., Novkovic, T., Sommer, H., Gilitschenski, I., Siegwart, R.: Evaluation of combined time-offset estimation and hand-eye calibration on robotic datasets. In: Hutter, M., Siegwart, R. (eds.) Proceedings of the 11th International Conference on Field and Service Robotics, Zurich, pp. 145–159. Springer, Cham (2018)Google Scholar
  6. Giftthaler, M., Sandy, T., Dörfler, K., Brooks, I., Buckingham, M., Rey, G., Kohler, M., Gramazio, F., Buchli, J.: Mobile robotic fabrication at 1:1 scale: the In situ Fabricator. Constr. Robot. 1(1–4), 3–14 (2017)CrossRefGoogle Scholar
  7. Hack, N., Lauer, W.V.: Mesh mould: robotically fabricated spatial meshes as reinforced concrete formwork. Archit. Des. 84(3), 44–53 (2014)Google Scholar
  8. Hack, N., Lauer, W.V., Langenberg, S., Gramazio, F., Kohler, M.: Overcoming repetition: robotic fabrication processes at a large scale. Int. J. Architectural Comput. (IJAC) 3(11), 285–299 (2013)CrossRefGoogle Scholar
  9. Hutchinson, S., Hager, G.D., Corke, P.I.: A tutorial on visual servo control. IEEE Trans. Robot. Autom. 12(1), 649–774 (1996)Google Scholar
  10. Laarman, J., Jokic, S., Novikov, P., Fraguada, L.E., Markopoulou, A.: Anti-gravity additive manufacturing. In: Gramazio, F., Kohler, M., Langenberg, S. (eds.) Fabricate: Negotiating Design & Making, pp. 192–197. UCL Press, London (2014)Google Scholar
  11. Lloret, E., Shahab, A.R., Linus, M., Flatt, R., Gramazio, F., Kohler, M., Langenberg, S.: Complex concrete structures: merging existing casting techniques with digital fabrication. Comput. Aided Des. 60, 40–49 (2015)CrossRefGoogle Scholar
  12. McGee, W., Thun, G., Velikov, K., Tish, D.: Infundibuliforms: kinetic systems, additive manufacturing, and tensile surface control. In: Sheil, B., Menges, A., Glynn, R., Skavara, M. (eds.) Fabricate: Rethinking Design and Construction, pp. 84–91. UCL Press, London (2017)Google Scholar
  13. Moravec, H.: Sensor fusion in certainty grids for mobile robots. AI Mag. 9(2), 61–74 (1988)Google Scholar
  14. Paul, G., Kirchner, N., Liu, D.K., Dissanayake, G.: An effective approach to simultaneous mapping and surface-type identification of complex 3D environments. J. Field Robot. 26(11–12 SI), 915–933 (2009)Google Scholar
  15. Paul, G., Quin, P., To, A.W.K., Liu, D.K.: A sliding window approach to exploration for 3D map building using a biologically inspired bridge inspection robot. In: Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 1097–1102. Shenyang (2015)Google Scholar
  16. Piker, D.: Kangaroo: form finding with computational physics. Archit. Des. 83(2), 136–137 (2013)Google Scholar
  17. Rodrigue, H., Bhandari, B., Wang, W., Ahn, S.H.: 3D soft lithography: a fabrication process for thermocurable polymers. J. Mater. Process. Technol. 217, 302–309 (2015)CrossRefGoogle Scholar
  18. Soler, V., Retsin, G., Jimenez Garcia, M.: A generalized approach to non-layered fused filament fabrication. In: Nagakura, T., Tibbits, S., Mueller, C., Ibañez, M. (eds.) Acadia 2017: Disciplines & Disruption, Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture, pp. 562–571. Cambridge, MA. (2017) Google Scholar
  19. Stepan, P., Kulich, M., Preucil, L.: Robust data fusion with occupancy grid. IEEE Trans. Syst. Man Cybern. Part C 35(1), 106–115 (2005)CrossRefGoogle Scholar
  20. Strobl, K.H., Hirzinger, G.: Optimal hand-eye calibration. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4647–4653. Beijing (2006)Google Scholar
  21. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  22. Vidal-Calleja, T., Andrade-Cetto, J., Sanfeliu, A.: Action selection for single camera SLAM. IEEE Trans. Syst. Man Cybern. Part B 40(6), 1567–1581 (2010)CrossRefGoogle Scholar
  23. Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the 1999 IEEE International Conference on Computer Vision, vol. 1, pp. 666–673. Kerkyra, Greece (1999)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sheila Sutjipto
    • 1
  • Daniel Tish
    • 2
  • Gavin Paul
    • 1
  • Teresa Vidal-Calleja
    • 1
  • Tim Schork
    • 2
    Email author
  1. 1.Centre for Autonomous Systems, Faculty of Engineering and Information Technology (FEIT)University of Technology, SydneyBroadwayAustralia
  2. 2.School of Architecture, Faculty of Design, Architecture and Building (DAB)University of Technology, SydneyBroadwayAustralia

Personalised recommendations