TY - GEN
T1 - Fire Frontline Monitoring by Enabling UAV-Based Virtual Reality with Adaptive Imaging Rate
AU - Islam, Shafkat
AU - Huang, Qiyuan
AU - Afghah, Fatemeh
AU - Fule, Peter
AU - Razi, Abolfazl
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Recently, using drones for forest fire management has gained a lot of attention from the research community due to their advantages such as low operation and deployment cost, flexible mobility, and high-quality imaging. It also minimizes human intervention, especially in hard-to-reach areas where the use of ground-based infrastructure is troublesome. Drones can provide virtual reality to firefighters by collecting on-demand high-resolution images with adjustable zoom, focus, and perspective to improve fire control and eliminate human hazards. In this paper, we propose a novel model for fire expansion as well as a distributed algorithm for drones to relocate themselves towards the front-line of an expanding fire field. The proposed algorithm comprises a light-weight image processing for fire edge detection that is highly desirable over computational expensive deep learning methods for resource-constrained drones. The positioning algorithm includes motions tangential and normal to fire frontline to follow the fire expansion while keeping minimum pairwise distances for collision avoidance and non-overlapping imaging. We proposed an action-reward mechanism to adjust the drones' speed and processing rate based on the fire expansion rate and the available onboard processing power. Simulations results are provided to support the efficacy of the proposed algorithm.
AB - Recently, using drones for forest fire management has gained a lot of attention from the research community due to their advantages such as low operation and deployment cost, flexible mobility, and high-quality imaging. It also minimizes human intervention, especially in hard-to-reach areas where the use of ground-based infrastructure is troublesome. Drones can provide virtual reality to firefighters by collecting on-demand high-resolution images with adjustable zoom, focus, and perspective to improve fire control and eliminate human hazards. In this paper, we propose a novel model for fire expansion as well as a distributed algorithm for drones to relocate themselves towards the front-line of an expanding fire field. The proposed algorithm comprises a light-weight image processing for fire edge detection that is highly desirable over computational expensive deep learning methods for resource-constrained drones. The positioning algorithm includes motions tangential and normal to fire frontline to follow the fire expansion while keeping minimum pairwise distances for collision avoidance and non-overlapping imaging. We proposed an action-reward mechanism to adjust the drones' speed and processing rate based on the fire expansion rate and the available onboard processing power. Simulations results are provided to support the efficacy of the proposed algorithm.
KW - UAV networks
KW - autonomous control
KW - fire monitoring
KW - image-based edge detection
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85083345986&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083345986&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF44664.2019.9049048
DO - 10.1109/IEEECONF44664.2019.9049048
M3 - Conference contribution
AN - SCOPUS:85083345986
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 368
EP - 372
BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Y2 - 3 November 2019 through 6 November 2019
ER -