In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone that employs observer drones potentially with different sensing and imaging capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include: i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system- without the need for inter-coalition communications- approaches that of a centrally-optimized system.
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Fire Frontline Monitoring by Enabling UAV-Based Virtual Reality with Adaptive Imaging Rate
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 ondemand 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.
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- Award ID(s):
- 1755984
- PAR ID:
- 10133290
- Date Published:
- Journal Name:
- Asilomar Conference on Signals, Systems, and Computers
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Due to Wildfire's huge destructive impacts on agriculture and food production, wildlife habitat, climate, human life and ecosystem, timely discovery of fires enable swift response to fires before they go out of control, in order to minimize the resulting damage and impacts. One of the emerging technologies for fire monitoring is deploying Unmanned Aerial Vehicles, due to their high flexibility and maneuverability, less human risk, and on-demand high quality imaging capabilities. In order to realize a real-time system for fire detection and expansion analysis, fast and high-accuracy image-processing algorithms are required. Several studies have shown that deep learning methods can provide the most accurate response, however the training time can be prohibitively long, especially when using online learning for constant refinement of the developed model. Another challenge is the lack of large datasets for training a deep learning algorithm. In this respect, we propose to use a pretrained mobileNetV2 architecture to implement transfer learning, which requires a smaller dataset and reduces the computational complexity while not compromising the accuracy. In addition, we conduct an effective data augmentation pipeline to simulate some extreme scenarios, which could promise the robustness of our approach. The testing results illustrate that our method maintains a high identification accuracy in different situations - original dataset (99.7%), adding Gaussian blurred (95.3%), and additive Gaussian noise (99.3%).more » « less
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