Hostname: page-component-7479d7b7d-pfhbr Total loading time: 0 Render date: 2024-07-12T17:16:32.790Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks

Published online by Cambridge University Press:  07 October 2015

Odysseas Kontovourkis ,
Marios C. Phocas  and
Ifigenia Lamprou
Odysseas Kontovourkis*
Affiliation:
Department of Architecture, University of Cyprus, Nicosia, Cyprus
Marios C. Phocas
Affiliation:
Department of Architecture, University of Cyprus, Nicosia, Cyprus
Ifigenia Lamprou
Affiliation:
Department of Architecture, University of Cyprus, Nicosia, Cyprus
*
Reprint requests to: Odysseas Kontovourkis, Department of Architecture, Faculty of Engineering, University of Cyprus, PO Box 20537, Nicosia 1678, Cyprus. E-mail: kontovourkis.odysseas@ucy.ac.cy
Get access Rights & Permissions [Opens in a new window]

Abstract

Nowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

Keywords

AdaptationArtificial Neural NetworksKinetic StructureMachine LearningOptimization
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 371 - 391
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahlquist, S., & Menges, A. (2010). Realizing formal and functional complexity for structurally dynamic systems in rapid computational means: computational methodology based on particle systems for complex tension-active form generation. In Advances in Architectural Geometry (Ceccato, C., Hesselgren, L., Pauly, M., Pottmann, H., & Wallner, J., Eds.), pp. 205–220. Vienna: Springer.Google Scholar
Beer, R.D., & Gallagher, J.C. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1(1), 91–122.CrossRefGoogle Scholar
Bentley, P.J. (1999). Evolutionary Design by Computers. San Francisco, CA: Morgan Kaufmann.Google Scholar
Bischoff, B., Nguyen-Tuong, D., Lee, I-H., Streichert, F., & Knoll, A. (2013). Hierarchical reinforcement learning for robot navigation. Proc. European Symp. Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN ’13, Bruges, Belgium, April 24–26.Google Scholar
Bischoff, B., Nguyen-Tuong, D., van Hoof, H., McHutchon, A., Rasmussen, C.E., Knoll, A., Peters, J., & Deisenroth, M.P. (2014). Policy search for learning robot control using sparse data. Proc. 2014 IEEE Int. Conf. Robotics and Automation (ICRA). Hong Kong: IEEE.Google Scholar
Brooks, R.A. (1991). Intelligence without representation. Artificial Intelligence 47(1–3), 139–159.CrossRefGoogle Scholar
Engelbrecht, A.P. (2007). Computational Intelligence: An Introduction, 2nd ed.West Sussex: Wiley.CrossRefGoogle Scholar
Fest, E., Shea, K., & Smith, I.F.C. (2004). Active tensegrity structure. Journal of Structural Engineering 130(10), 1454–1465.CrossRefGoogle Scholar
Festo AG & Co. KG. (2010). Pneumatic Lightweight Structures. Accessed at http://www.festo.com/rep/en_corp/assets/pdf/Pneumatic_lightweight_structures.pdf on March 3, 2014.Google Scholar
Festo AG & Co. KG. (2011). Robotino XT. Accessed at http://www.festo.com/net/SupportPortal/Files/46266/Brosch_robotinoXT_en_RZ_110311_lo_einzel.pdf on March 3, 2014.Google Scholar
Festo AG & Co. KG. (2013). Learning Gripper: Gripping and Positioning Through Independent Learning. Accessed at https://www.festo.com/net/SupportPortal/Files/248131/54819_Broschuere_LearningGripper_en_130322_lo_L.pdf on March 3, 2014.Google Scholar
Fleischmann, M., Knippers, J., Lienhard, J., Menges, A., & Schleicher, S. (2012). Material behaviour: embedding physical properties in computational design processes. In Architectural Design, Material Behaviour: Embedding Physical Properties in Computational Design Processes (Menges, A., Ed.), pp. 44–51. London: Wiley.Google Scholar
Fleischmann, M., & Menges, A. (2012). Physics-based modeling as an alternative approach to geometrical constrain-modeling for the design of elastically-deformable material systems. Proc. Digital Physicality/Physical Digitality: 30th eCAADe Conference, Vol. 1, pp. 565–575. Prague: Czech Technical University in Prague.CrossRefGoogle Scholar
Fox, M.A. (2003). Kinetic architectural systems design. In Transportable Environments: Book 2 (Kronenburg, R., Ed.), pp. 163–186. London: Taylor & Francis.Google Scholar
Fox, M., & Kemp, M. (2009). Interactive Architecture. New York: Princeton Architectural Press.Google Scholar
Frazer, J. (1995). An Evolutionary Architecture. London: Architectural Association.Google Scholar
Haque, U. (2007). The architectural relevance of Gordon Pask. In Architectural Design: Vol. 4. Social–Interactive Design Environments (Bullivant, L., Ed.), pp. 54–61. London: Wiley.Google Scholar
Hebb, D.O. (1949). The Organization of Behavior. New York: Wiley.Google Scholar
Hernandez, J.S., Skelton, R., & Mirats, T.J. (2009). Dynamically stable collision avoidance for tensegrity based robots. Proc. Reconfigurable Mechanisms and Robots ReMAR ASME/IFToMM Int. Conf., pp. 315–322. London: IEEE.Google Scholar
Hoberman, C. (1993). Unfolding architecture: an object that is identically a structure and a Mechanism. Architectural Design 63, 56–59.Google Scholar
Holland, J. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Hopfield, J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences in United States of America 79(8), 2554–2558.CrossRefGoogle ScholarPubMed
Hurvey, I., Husbands, P., Cliff, D., Thompson, A., & Jakobi, N. (1997). Evolutionary robotics: the Sussex approach. Robotics and Autonomous Systems 20(2–4), 205–224.CrossRefGoogle Scholar
Jain, A.K. (1966). Artificial neural networks: a tutorial. Computer 29(3), 31–44.CrossRefGoogle Scholar
Korkmaz, S., Hadj Ali, N.B., & Smith, I.F.C. (2011). Determining control strategies for damage tolerance of an active tensegrity structure. Engineering Structures 33(6), 1930–1939.CrossRefGoogle Scholar
Kostanic, I., & Ham, F.M. (2000). Principles of Neurocomputing for Science and Engineering. Boston: McGraw–Hill.Google Scholar
Laschi, C., Mazzolai, B., Cianchetti, M., & Dario, P. (2009). Design of a biomimetic robotic octopus arm. Bioinspiration and Biomimetics 4(1), 015006.CrossRefGoogle ScholarPubMed
Majidi, C., Shepherd, R.F., Kramer, R.K., Whitesides, G.M., & Wood, R.J. (2013). Influence of surface traction on soft robot undulation. International Journal of Robotic Research 32(13), 1577–1584.CrossRefGoogle Scholar
McCulloch, W.S., & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133.CrossRefGoogle Scholar
Mehanna, R. (2013). Resilient structures through machine learning and evolution. Proc. 33rd Annual Conf. Association for Computer Aided Design in Architecture, ACADIA ’13, pp. 319–326. Cambridge, October 24–26, 2013.CrossRefGoogle Scholar
Mitchell, W.J. (1990). The Logic of Architecture—Design, Computation and Cognition. Cambridge, MA: MIT Press.Google Scholar
Montana, D.J. (1995). Neural network weight selection using genetic algorithms. In Intelligent Hybrid Systems (Goonatilake, S., & Khebbal, S., Eds.), pp. 85–150. Sussex: Wiley.Google Scholar
Negroponte, N. (1970). The Architecture Machine: Towards a More Human Environment. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Neumann, K., Rolf, M., Reinhart, R.F., Queisser, J., & Steil, J.J. (2014). Learning to control the bionic handling assistant. Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Hong Kong, May 31–June 5.Google Scholar
Phocas, M.C., Kontovourkis, O., & Ioannou, T. (2012). Interdisciplinary research-based design: the case of a kinetic form-active tensile membrane. Architectural Engineering Technology 1(2).Google Scholar
Price, C. (2001). Gordon Pask. Kybernetes 30(5–6), 819–820.CrossRefGoogle Scholar
Rosenblatt, R. (1962). Principles of Neurodynamics. New York: Spartan Books.Google Scholar
Rost, A., & Schadle, S. (2013). The SLS-generated soft robotic hand—an integrated approach using additive manufacturing and reinforcement learning. Proc. 12th Int. Conf. Machine Learning and Applications (ICMLA) (1), pp. 215–220. Miami: IEEE.CrossRefGoogle Scholar
Rumelhart, D.E., & McClelland, J.L. (1986). Parallel Distributed Processing: Exploration in the Microstructure of Cognition. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Steltz, E., Mozeika, A., Rodenberg, N., Brown, E., & Jaeger, H.M. (2009). JSEL: jamming skin enabled locomotion. Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 5672–5677. St. Louis, MO: IEEE.CrossRefGoogle Scholar
Sutton, R.S., & Barto, A.G. (1998). Reinforcement Learning: An introduction. Cambridge, MA: MIT Press.Google Scholar
Thompson, W.D. (1961). On Growth and Form. New York: Cambridge University Press.Google Scholar
Turing, A. M. (1936). On computable numbers with an application to the Entscheidungs problem. Proceedings of the London Mathematical Society 42(2), 230–265.Google Scholar
Von Neumann, J. (1966). The Theory of Self-Reproducing Automata. Urbana, IL: University of Illinois Press.Google Scholar
Wada, B.K., Fanson, J.L., & Crawley, E.F. (1990). Adaptive structures. Journal of Intelligent Materials Systems and Structures 1(2), 157–174.CrossRefGoogle Scholar
Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioural Sciences. PhD Thesis. Harvard University.Google Scholar
Wiener, N. (1961). Cybernetics or Control and Communication in the Animal and the Machine, 2nd ed.Cambridge, MA: MIT Press.Google Scholar
Zuk, W., & Clark, R.H. (1970). Kinetic Architecture. New York: Van Nostrand–Reinhold.Google Scholar