48,362 research outputs found
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving
Autonomous driving confronts great challenges in complex traffic scenarios,
where the risk of Safety of the Intended Functionality (SOTIF) can be triggered
by the dynamic operational environment and system insufficiencies. The SOTIF
risk is reflected not only intuitively in the collision risk with objects
outside the autonomous vehicles (AVs), but also inherently in the performance
limitation risk of the implemented algorithms themselves. How to minimize the
SOTIF risk for autonomous driving is currently a critical, difficult, and
unresolved issue. Therefore, this paper proposes the "Self-Surveillance and
Self-Adaption System" as a systematic approach to online minimize the SOTIF
risk, which aims to provide a systematic solution for monitoring,
quantification, and mitigation of inherent and external risks. The core of this
system is the risk monitoring of the implemented artificial intelligence
algorithms within the AV. As a demonstration of the Self-Surveillance and
Self-Adaption System, the risk monitoring of the perception algorithm, i.e.,
YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and
external collision risk are jointly quantified via SOTIF entropy, which is then
propagated downstream to the decision-making module and mitigated. Finally,
several challenging scenarios are demonstrated, and the Hardware-in-the-Loop
experiments are conducted to verify the efficiency and effectiveness of the
system. The results demonstrate that the Self-Surveillance and Self-Adaption
System enables dependable online monitoring, quantification, and mitigation of
SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
A major challenge with off-road autonomous navigation is the lack of maps or
road markings that can be used to plan a path for autonomous robots. Classical
path planning methods mostly assume a perfectly known environment without
accounting for the inherent perception and sensing uncertainty from detecting
terrain and obstacles in off-road environments. Recent work in computer vision
and deep neural networks has advanced the capability of terrain traversability
segmentation from raw images; however, the feasibility of using these noisy
segmentation maps for navigation and path planning has not been adequately
explored. To address this problem, this research proposes an uncertainty-aware
path planning method, URA* using aerial images for autonomous navigation in
off-road environments. An ensemble convolutional neural network (CNN) model is
first used to perform pixel-level traversability estimation from aerial images
of the region of interest. The traversability predictions are represented as a
grid of traversal probability values. An uncertainty-aware planner is then
applied to compute the best path from a start point to a goal point given these
noisy traversal probability estimates. The proposed planner also incorporates
replanning techniques to allow rapid replanning during online robot operation.
The proposed method is evaluated on the Massachusetts Road Dataset, the
DeepGlobe dataset, as well as a dataset of aerial images from off-road proving
grounds at Mississippi State University. Results show that the proposed image
segmentation and planning methods outperform conventional planning algorithms
in terms of the quality and feasibility of the initial path, as well as the
quality of replanned paths
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