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

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

Liu Cheng, Alexander (TU Delft Digital Architecture; GRAFT Gesellschaft von Architekten)
Bier, H.H. (TU Delft Digital Architecture; Anhalt University of Applied Sciences Dessau)
Mostafavi, Sina (TU Delft Digital Architecture; Anhalt University of Applied Sciences Dessau)

2017

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.

Robots
Object recognition
Cameras
Shape
Visualization
Concrete
Machine learning

http://resolver.tudelft.nl/uuid:af0882e3-36a0-4ce9-8dd7-c53c935e9f88

https://doi.org/10.1109/ETCM.2017.8247495

IEEE

978-1-5386-3894-1

Proceedings of the 2nd IEEE Ecuador Technical Chapters Meeting (ETCM 2017)

ETCM 2017: 2nd IEEE Ecuador Technical Chapters Meeting 2017, 2017-10-18 → 2017-10-20, Hotel Barceló, Salinas, Ecuador

Accepted Author Manuscript

Institutional Repository

conference paper

© 2017 Alexander Liu Cheng, H.H. Bier, Sina Mostafavi