Related papers: Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke

Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke

URL: http://arxiv.org/abs/2312.03521v1
Date: Wed, 6 Dec 2023 14:30:15 GMT
Title: Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke
Authors: Chang Liu and Tamas Sziranyi
Abstract summary: The utilization of unmanned aerial vehicles (UAVs) has shown promise in aiding wildfire management efforts. This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones.
Score: 3.9561033879611944
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In recent years, the increasing prevalence and intensity of wildfires have posed significant challenges to emergency response teams. The utilization of unmanned aerial vehicles (UAVs), commonly known as drones, has shown promise in aiding wildfire management efforts. This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones, considering dynamic fire and smoke models. First, the location of the source of the wildfire can be well located by information fusion between UAV and satellite, and the road conditions in the vicinity of the fire can be assessed and analyzed using multi-channel remote sensing data. Second, the road network can be extracted and segmented in real time using UAV vision technology, and each road in the road network map can be given priority based on the results of road condition classification. Third, the spread model of dynamic fires calculates the new location of the fire source based on the fire intensity, wind speed and direction, and the radius increases as the wildfire spreads. Smoke is generated around the fire source to create a visual representation of a burning fire. Finally, based on the improved A* algorithm, which considers all the above factors, the UAV can quickly plan an escape route based on the starting and destination locations that avoid the location of the fire source and the area where it is spreading. By considering dynamic fire and smoke models, the proposed system enhances the safety and efficiency of drone operations in wildfire environments.

Related papers

A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States [44.99833362998488]
This research investigates proactive methods for detecting and handling wildfires in the United States. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology. Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management.
arXiv Detail & Related papers (2024-02-27T04:09:30Z)
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management [6.043705525669726]
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management.
arXiv Detail & Related papers (2024-01-04T05:09:35Z)
Active Wildfires Detection and Dynamic Escape Routes Planning for Humans through Information Fusion between Drones and Satellites [3.9561033879611944]
This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection and road networks extraction. Fire source location and the segmentation of smoke and flames are targeted based on Sentinel 2 satellite imagery. The results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire.
arXiv Detail & Related papers (2023-12-06T14:25:47Z)
Image-Based Fire Detection in Industrial Environments with YOLOv4 [53.180678723280145]
This work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector.
arXiv Detail & Related papers (2022-12-09T11:32:36Z)
Assimilation of Satellite Active Fires Data [0.0]
The aim of this thesis is to develop techniques to help combat the impacts of wildfires by improving wildfire modeling capabilities. In particular, we develop a method for constructing the history of a fire, a new technique for assimilating wildfire data, and a method for modifying the behavior of a modeled fire.
arXiv Detail & Related papers (2022-04-01T20:11:28Z)
Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks [16.782309873372057]
This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets. Spatio-temporal split learning is applied to this scenario to preserve privacy and globally train a fire classification model. This paper explores the adequate number of clients and data ratios for split learning in this UAV setting, as well as the required network infrastructure.
arXiv Detail & Related papers (2021-11-15T01:39:31Z)
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images [128.881857704338]
We study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image. We show that the method can be extended to detect dynamic objects on the BEV plane. We validate our approach against powerful baselines and show that our network achieves superior performance.
arXiv Detail & Related papers (2021-10-05T12:40:33Z)
A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
arXiv Detail & Related papers (2021-07-19T12:54:04Z)
From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z)
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
arXiv Detail & Related papers (2021-03-03T20:54:19Z)
Unmanned Aerial Systems for Wildland and Forest Fires [0.0]
Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Unmanned Aerial Systems (UAS) have proven to be useful due to their maneuverability.
arXiv Detail & Related papers (2020-04-28T23:01:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.