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Autonomous Lighting Audits: Part 2 — Light Identification and Analysis

[+] Author Affiliations
Trevor J. Terrill, Christopher J. Bay, Bryan P. Rasmussen

Texas A&M University, College Station, TX

Paper No. DSCC2014-6126, pp. V001T17A004; 10 pages
doi:10.1115/DSCC2014-6126
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4618-6
  • Copyright © 2014 by ASME

abstract

Buildings are responsible for approximately 40% of all US energy use and carbon emissions. There exists large potential to improve building efficiency through retro-commissioning, but expense and required expertise of building auditors limit current implementation. Autonomous robotic assessments have the potential to provide consistent building energy audits with reduced cost and enhanced capabilities. As a first step in automating building audits, this paper presents work on automating the lighting analysis of a building.

As an aerial vehicle navigates and explores a room, the prototype system captures images and collects spectrometer readings. These data are used to quantify and classify lighting in a room. Additionally, images acquired from the optical camera are merged to form a composite image of the area. This composite image is used for navigation to lights to record spectrometer readings. Lighting type is then classified from these spectrometer readings. The combined lighting quantification and classification is used to create a topology map of light levels. The combined data are used to perform a thorough analysis of lighting and make lighting recommendations.

Copyright © 2014 by ASME

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