Abstract
In this paper, we discuss a robotic-assisted concrete finishing method for fabricating architectural panels. Concrete finishing is an important process for producing architectural elements with acceptable surface quality. It is also a challenging process conventionally relying on skillful laborers. We describe a hybrid framework incorporating both human skill and robotics in the concrete finishing process and a multi-phase sensing strategy to assist in part touch-up and to validate final surface quality. The paper discusses a general approach to finishing from three perspectives: (1) Material characterization of concrete’s workability throughout its setting process, (2) A modular system-architecture for collaborative human-robot concrete finishing, and (3) Assessment feedback of surface quality using process images.
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References
Abdel-Qader I, Abudayyeh O, Kelly ME (2003) Analysis of edge-detection techniques for crack identification in bridges. J Comput Civil Eng 17(4):255–263
Bard J, Tursky R, Jeffers M (2016) A Survey of Surface Scanning Techniques for Architectural Substrates in Robotic Assembly. Roboti Fabric Architect Art Design 2016:263
Bard J, Cupkova D, Washburn N, Zeglin G (2018a) Robotic concrete surface finishing: a moldless approach to creating thermally tuned surface geometry for architectural building components using Profile-3D-printing. Construct Robot 2(1–4):53–65
Bard J, Cupkova D, Washburn N, Zeglin G (2018b) Robotic concrete surface finishing: a moldless approach to creating thermally tuned surface geometry for architectural building components using Profile-3D-Printing. Construct Robot 2(1–4):53–65
Bard J, Cupkova D, Washburn N, Zeglin G (2018c) Thermally informed robotic topologies: profile-3d-printing for the robotic construction of concrete panels, thermally tuned through high resolution surface geometry. In: Robotic Fabrication in Architecture, Art and Design, pages 113–125
Bard J, Bidgoli A, Chi WW (2018d) Image classification for robotic plastering with convolutional neural network. Robot Fabric Architect Art Design: 3–15
Bruckermann O, Alberdi JA (2010) Structural design of the DRL-10 Space Pavilion. J Architect Eng 16(3):112–118
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698
Cupkova D, Promoppatum P (2017) Modulating thermal mass behavior through surface figuration
Ferraris CF (1999) Measurement of the rheological properties of high performance concrete: state of the art report. J Res Natl Inst Stand Technol 104(5):461
Ferraris CF, Brower LE, Banfill P Beaupré D, Chapdelaine F, de Larrard F, Domone P, Nachbaur L, Sedran , Wallevik O (2001) Comparison of concrete rheometers: international test at LCPC (Nantes, France) in October, 2000. US Department of Commerce, National Institute of Standards and Technology
Giftthaler M, Sandy T, Dörfler K, Brooks I, Buckingham M, Rey G, Kohler M, Gramazio F, Buchli J (2017) Mobile robotic fabrication at 1: 1 scale: the in situ fabricator. Construct Robot 1(1–4):3–14
Hack N, Kloft H (2020) Shotcrete 3d printing technology for the fabrication of slender fully reinforced freeform concrete elements with high surface quality: a real-scale demonstrator. In: RILEM International Conference on Concrete and Digital Fabrication, pages 1128–1137. Springer
Hutchinson TC, Chen ZQ (2006) Improved image analysis for evaluating concrete damage. J Comput Civil Eng 20(3):210–216
Lim S, Buswell RA, Le TT, Austin SA, Gibb AGF, Thorpe Tony (2012) Developments in construction-scale additive manufacturing processes. Automat Construct 21:262–268
Mehta PK (1986) Concrete. Structure Prop Mater
Neudecker S, Bruns C, Gerbers R, Heyn J, Dietrich F, Dröder K, Raatz A, Kloft H (2016) A new robotic spray technology for generative manufacturing of complex concrete structures without formwork. Procedia CIRP 43:333–338
Paolini A, Kollmannsberger S, Rank E (2019) Additive manufacturing in construction: a review on processes, applications, and digital planning methods. Add Manuf 30:100894
Ritchie AGB (1962) The triaxial testing of fresh concrete. Mag Conc Res 14(40):37–42
Sinha SK, Fieguth PW (2006) Segmentation of buried concrete pipe images. Autom Construct 15(1):47–57
Spahr R, Johnston D (2014) The New “guide to formed concrete surfaces”. Conc Int 36(6):30–32
Spanos G, Daehn G, Allison J, Bilitz E, Bourne D, Cao J, Clarke K, Johnnie D Jr, Ed H, Lewandowski J (2019) Metamorphic manufacturing: shaping the future of on-demand components
Suwwanakarn S, Zhu Z, Brilakis I (2007) Automated air pockets detection for architectural concrete inspection
Tan RT, Ikeuchi K (2005) Reflection components decomposition of textured surfaces using linear basis functions. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 125–131. IEEE
Tappen MF (2014) Intrinsic images
Tattersall GH (1976) The workability of concrete
Wangler T, Lloret E, Reiter L, Hack N, Gramazio F, Kohler Matthias, Bernhard Mathias, Dillenburger Benjamin, Buchli Jonas, Roussel Nicolas (2016) Digital concrete: opportunities and challenges. RILEM Tech Lett 1:67–75
Yang Q, Wang S, Ahuja N (2010) Real-time specular highlight removal using bilateral filtering. In: European conference on computer vision, pages 87–100. Springer
Zhang C, Zhang Z (2016) Calibration between depth and color sensors for depth cameras, February 23 2016. US Patent 9,270,974
Acknowledgements
The funding for the project is provided by Manufacturing Futures Initiative and Pennsylvania Manufacturing Innovation Program.The authors would like to thank the participating partners Prof. David Bourne, Stanislaw Åżelazny, Maciej Kolek from The Robotics Institute Carnegie Mellon University.
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Liu, J., Lee, Yc. & Bard, J. Material characterization of workability and process imaging for robotic concrete finishing. Constr Robot 5, 73–85 (2021). https://doi.org/10.1007/s41693-021-00058-9
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DOI: https://doi.org/10.1007/s41693-021-00058-9