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Autonomous Lighting Audits: Part 1 — Building Navigation and Mapping

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

Texas A&M University, College Station, TX

Paper No. DSCC2014-6125, pp. V001T17A003; 10 pages
doi:10.1115/DSCC2014-6125
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 represent a large portion, approximately 40%, of all US energy use and carbon emissions. Significant savings can be found by conducting energy audits of the buildings, but the extensive training and cost of the audits prevent more widespread use. Automating the audit process with robots can greatly reduce the cost and provide more information to give better recommendations. This paper is the first in a series that proposes a system of autonomous robots that can conduct energy audits.

Specifically, this paper presents an overview of the autonomous system and details an unmanned aerial vehicle (UAV) platform which is used to perform automated lighting audits. Also, modifications to an existing exploration algorithm are proposed that will allow autonomous exploration of an unknown, GPS-denied environment while identifying and navigating to targets in real-time. This new algorithm is called SRT-Target. The UAV navigates to the lights, the target objects, in order to take additional measurements so that the light type can be determined. Movement of the UAV can be limited by a calibration factor β to account for sensor capabilities of the target sensor. Simulations of the algorithm show the exploration of the unknown area and the UAV moving to targets as they are identified.

Copyright © 2014 by ASME

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