Deep Learning Object-Recognition in a Design-to-Robotic-Production and -Operation Implementation

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This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-Robotic-Production and -Operation (D2RP&O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized design-to-production framework informed by optimization strategies based on point clouds. With respect to D2RO, said implementation builds on a previously developed highly heterogeneous, partially meshed, self-healing, and Machine Learning (ML) enabled Wireless Sensor and Actuator Network (WSAN). In this instance, a computer vision mechanism based on open-source Deep Learning (DL) / Convolutional Neural Networks (CNNs) for object-recognition is added to the inherited ecosystem. This mechanism is integrated into the system’s Fall-Detection and -Intervention System in order to enable decentralized detection of three types of events and to instantiate corresponding interventions. The first type pertains to human-centered activities / accidents, where cellular- and internet-based intervention notifications are generated in response. The second pertains to object-centered events that require the physical intervention of an automated robotic agent. Finally, the third pertains to object-centered events that elicit visual / aural notification cues for human feedback. These features, in conjunction with their enabling architectures, are intended as essential components in the on-going development of highly sophisticated alternatives to existing Ambient Intelligence (AmI) solutions.
Original languageEnglish
Title of host publicationProceedings of the 2nd IEEE Ecuador Technical Chapters Meeting (ETCM 2017)
Number of pages6
ISBN (Print)978-1-5386-3894-1
Publication statusPublished - 2017
EventETCM 2017: 2nd IEEE Ecuador Technical Chapters Meeting 2017 - Hotel Barceló, Salinas, Ecuador
Duration: 18 Oct 201720 Oct 2017


ConferenceETCM 2017: 2nd IEEE Ecuador Technical Chapters Meeting 2017

Bibliographical note

Accepted Author Manuscript


  • Robots
  • Object recognition
  • Cameras
  • Shape
  • Visualization
  • Concrete
  • Machine learning


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