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Autonomous Fire Suppression System for Use in High and Low Visibility Environments by Visual Servoing
- Source :
- Fire Technology. 52:1343-1368
- Publication Year :
- 2016
- Publisher :
- Springer Science and Business Media LLC, 2016.
-
Abstract
- An autonomous fire suppression system was developed for localized fire suppression in high and low visibility environments. The system contains a multispectral sensor suite, including UV sensors and infrared stereovision, to detect and target a fire for suppression. The UV sensor provides an alert to the system to begin fire detection. IR imagery is used to segment fire from the field of view and target the base of the fire and IR stereovision to determine the 3D coordinates of the fire. IR tracking provides continuously updated information on the size and intensity of the fire before and during suppression and alerts the system when to cease suppression activity. Visual servoing is used to correctly position a nozzle based on feedback of changes in the fire location and size. The autonomous system was used to suppress wood crib fires (40 kW to 50 kW) in high and low visibility environments and at varying distances (2.8 m to 5.5 m) and elevations (0.4 m to 1.3 m). The suppression time in clear conditions was 3.72 s ± 1.51 s and 4.49 s ± 1.62 s in low visibility conditions. To simulate wind effects and inaccurate initial target coordinates, forced offsets were input to the system to show effectiveness of the feedback control algorithm when an initial estimate of spray trajectory does not accurately spray the center base of the fire. System performance with a forced offset resulted in suppression times of 4.11 s ± 0.84 s.
- Subjects :
- Offset (computer science)
Computer science
Fire detection
business.industry
Multispectral image
Poison control
020101 civil engineering
Field of view
02 engineering and technology
Visual servoing
0201 civil engineering
Fire suppression system
Fire protection
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
General Materials Science
Computer vision
Artificial intelligence
Safety, Risk, Reliability and Quality
business
Remote sensing
Subjects
Details
- ISSN :
- 15728099 and 00152684
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- Fire Technology
- Accession number :
- edsair.doi...........6b7fa795f445d9dba207a505182dc588
- Full Text :
- https://doi.org/10.1007/s10694-016-0564-8