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- 09 AprHyperbody's METABODY team collaborates with the TU Delft Robotics Institute to develop the HYPER LOOP
- 26 MarHyperbody's Robotic Building (RB) team hosts Delft Robotics Institute's monthly organised RoboCafé.
- 20 FebHyperbody Guest Researcher Serban Bodea presents the Robotic 3D Printing project at the BEMNext colloquium, CiTG, TUDelft
- 19 FebAchilleas Psyllidis collaborates with the Amsterdam Institute for Advanced Metropolitan Solutions (AMS)
- 09 FebRobotic 3D printing project prototypes will be exhibited and presented at Week van De Bouw (Construction Week) in Utrecht
- 03 FebDr. Nimish Biloria lectures at the Design-Lab, Swedish School of Textiles, University of Boras, Sweden.
- 23 JanFinal Review MSc1&3 Vertical Studio: Continuous Variation (M4H, MerweVierhavens)
- 09 JanAchilleas Psyllidis and Delft Social Data Science Lab researchers present and participate at TU Delft's 173rd anniversary
- 12 DecSina Mostafavi lectures at AA school, Algorithms and Actualization Symposium
- 10 DecFootprint 15 edited by Henriette Bier (TUD) and Terry Knight (MIT) is now available online
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A.Liu Cheng, H. Bier, G. Latorre, B. Kemper and D. Fischer publish a paper on A High-Resolution Intelligence Implementation based on Design-to-Robotic-Production and -Operation strategies in the 34th International Symposium on Automation and Robotics in Construction (ISARC 2017) (June 28 - July 1, 2017).
ABSTRACT: This paper presents an initial proof-of-concept implementation of a comprehensively intelligent built-environment based on mutually informing Design-to-Robotic-Production and -Operation (D2RP&O) strategies and methods developed at Delft University of Technology (TUD). In this implementation, D2RP is expressed via deliberately differentiated and function-specialized components, while D2RO expressions subsume an extended Ambient Intelligence (AmI) enabled by a Cyber-Physical System (CPS). This CPS, in turn, is built on a heterogeneous, scalable, self-healing, and partially meshed Wireless Sensor and Actuator Network (WSAN) whose nodes may be clustered dynamically ad hoc to respond to varying computational needs. Two principal and innovative functionalities are demonstrated in this implementation: (1) cost-effective yet robust Human Activity Recognition (HAR) via Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classification models, and (2) appropriate corresponding reactions that promote the occupant’s spatial experience and well-being via continuous regulation of illumination with respect to colors and intensities to correspond to engaged activities. The present implementation attempts to provide a fundamentally different approach to intelligent built-environments, and to promote a highly sophisticated alternative to existing intelligent solutions whose disconnection between architectural considerations and computational services limits their operational scope and impact.