Robotic Building implies both physically built robotic environments and robotically supported building processes. Physically built robotic environments consist of reconfigurable, adaptive systems incorporating sensor-actuator mechanisms that enable buildings to interact with their users and surroundings in real-time. These robotic environments require Design-to-Production and-Operation (D2P&O) chains that may be (partially or completely) robotically driven. This chapter describes previous work aiming to integrate D2RP&O processes by linking performance-driven design with robotic production and user-driven building operation. 5.1 Introduction While architecture and architectural production are increasingly incorporating aspects of non-human agency employing data, information, and knowledge contained within the (worldwide) network connecting electronic devices, the question is not whether but how robotic systems can be incorporated into building processes and buildings (Oosterhuis and Bier 2013). This chapter aims to answer this question by reflecting on the achievements of the Robotic Building (RB) team at Technical University Delft (TU Delft) and by identifying future steps. The focus is on an architecture that is robotically enabled to interact with its users and surroundings in real-time and the corresponding Design-to-Production and-Operation (D2P&O) processes that are (in part or as whole) robotically driven. Such modes of production and operation involve agency of both humans and non-humans. Thus agency is not The original version of the book was revised: Open access text has been updated in FM, Chapter
-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 CyberPhysical 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) costeffective yet robust Human Activity Recognition (HAR) via Support Vector Machine (SVM) and kNearest Neighbor (k-NN) classification models, and (2) appropriate corresponding reactions that promote the occupant's spatial experience and wellbeing 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.
This paper presents the implementation of a facial-identity and-expression recognition mechanism that confirms or negates physical and/or computational actuations in an intelligent built-environment. Said mechanism is built via Google Brain's TensorFlow (as regards facial identity recognition) and Google Cloud Platform's Cloud Vision API (as regards facial gesture recognition); and it is integrated into the ongoing development of an intelligent built-environment framework, viz., Design-to-Robotic-Production &-Operation (D2RP&O), conceived at Delft University of Technology (TUD). The present work builds on the inherited technological ecosystem and technical functionality of the Design-to-Robotic-Operation (D2RO) component of said framework; and its implementation is validated via two scenarios (physical and computational). In the first scenario-and building on an inherited adaptive mechanism-if building-skin components perceive a rise in interior temperature levels, natural ventilation is promoted by increasing degrees of aperture. This measure is presently confirmed or negated by a corresponding facial expression on the part of the user in response to said reaction, which serves as an intuitive override / feedback mechanism to the intelligent building-skin mechanism's decision-making process. In the second scenario-and building on another inherited mechanism-if an accidental fall is detected and the user remains consciously or unconsciously collapsed, a series of automated emergency notifications (e.g., SMS, email, etc.) are sent to family and/or caretakers by particular mechanisms in the intelligent built-environment. The precision of this measure and its execution are presently confirmed by (a) identity detection of the victim, and (b) recognition of a reflexive facial gesture of pain and/or displeasure. The work presented in this paper promotes a considered relationship between the architecture of the builtenvironment and the Information and Communication Technologies (ICTs) embedded and/or deployed.
Robotic-Production and-Operation strategies. In Proceedings of the 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon (ISARC 2018): The Future of Building Things (pp. 1005-1012). [212] IAARC, International Association for Automation and Robotics in Construction. Important note To cite this publication, please use the final published version (if applicable). Please check the document version above.
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