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Rethinking wildfire with data analytics and systems design.  

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forestfirehub
(@forestfirehub)
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Firefighting Fleet Management Simulation

Fire seasons are running longer, stronger, hotter.

The trend is not going away. More firefighting equipment are, of course, needed. With the situation seemingly getting worse and not better, how can we turn a losing battle on its head?
One way is to apply some of our biggest tech innovations.

We decided to run a crowdsourcing effort.
Big data is already being used to understand and predict wildfire spread, but how can some of the other big technology trends potentially help?
If we can apply to the worsening problem of destructive wildfires some of the innovations from the tech sector’s best-funded, exciting and emerging technologies, we may have to opportunity to make a huge difference in response.
Technology has the potential to change that, facilitating collaboration through new channels and tools for conducting cross-disciplinary (Internet of things (IoT),Swarm intelligence (SI), Automato Quadruped Robotics and UAVs (Unmanned aerial vehicles) in a computer-simulated environment, predicting trends and building strategies to turn data into actionable insights.

This Forest Fire Hackathon was born out of that realization—that we needed to play catch up and find new models for autonomus firefighting to protect firefighters.

Ideas to highlight technology's impact and find solutions to forest fire.

A data-driven hackhaton to identify the most relevant solutions globally, as an open source thinking approach to wildfires.
Further into the future, artificial intelligence and autonomous robots are in a growing category of devices playing a larger role to fight fires on the ground will become commonplace. It’s not a giant leap to teach these systems how to spot patterns and conditions from images of vegetation that commonly lead to a fire breaking out or spreading rapidly.

It’s not much of a stretch to believe that such technologies will soon help in the battle to contain wildfires. Fast response are required.

Let's start a Cooperative work to accomplish shared goals, gathering and collaborating on innovation ideas. To create discussion to disseminate the adoption of new breakthrough technology. In a process of looking at forest fire in a new and unorthodox way, to provide new solutions.

Using data to find new concepts, like:
Models designed for mission-critical systems to identify potential issues related to autonomous firefighter robots. Environmental modeling simulation as an Experimental Approach to Optimal Aerosol Dispersion of fire extinguishing agent, via goal-oriented groups of drones and quadruped robot addressing Swarm intelligence (SI) for optimization.

Mission:
Run a crowdsourcing effort, to tap the collective wisdom, also opened an online suggestion box (forestfirehub.com). A space where people can discuss and develop ideas and software infrastructure to enable the next generation of drones and robotics companies to simulate firefighting robotic fleets. Develop a computer simulation modeling to test quadruped robot and autonomous drones and their swarming algorithms to understand and evaluate 'what if' case scenarios in the form of open-source software.

Idea validation:
Viability of automatically optimized aerosol fire extinguishing agent dispersion by drone and quadruped in dynamical system.
Getting a picture cost-effectively and without endangering humans could increase the efficacy of preemptive burning, reducing the impact to lives and property.
That will help ensure the autonomous robots will operate well in the real world.
In a computer simulation modeling the drones would fly over a burning area and,by examining the vegetation and wind direction and other factors, predict the fire's spread and direction. With that information they would then precisely drop retardant to stop the fire as Joint Coordination Activity with Quadruped Robots on Internet of Things.

What Types of Insights Are Possible?
The question is, how can you most effectively use computer simulation modeling to understand and evaluate 'what if' case scenarios to autonomous firefighting advantage?

Wildfires don’t yet have the equivalent of a grand unified model to explain their behavior.

You could argue that a wildfire is the most complicated natural disaster, because it’s both a product of atmospheric conditions—themselves extremely complex—and a manipulator of atmospheric conditions.

The contributing factors are just so different, and work on such different scales—air dynamics for one, the aridity of local vegetation for another.

But fire is to a large degree predictable.
It follows certain rules and prefers certain fuels and follows certain wind patterns.

A great challenge from the complexity of flow dynamics and the high cost of numerical simulation.

You can't optimize what you don't measure. There is not a one-size-fits-all solution. Every wildfire is different and should be treated in a unique way.

There is work yet to be done to improve the computer-generated fire images in the simulated environment for training the artificial intelligence, self-organising swarms of drones and quadruped robot designed to engage in autonomous fire suppression in decentralised multi-robot systems.

Pick your favorite strategy to Computer-Simulated Environment for Modeling and Dynamic- Behavior-Analysis to extract insights from machine learning models.
Modeling firefighting with reinforcement learning, neural networks (CNN), game theory, machine-learning algorithms, and other techniques, to predict the behavior of wildfire and plan optimal approaches for containment.

Computer models using data, math and computer instructions to predict events in the real world.

Essentially rule-based structures for making decisions.

In general, conforming to commercially available tech, specification, standards and current regulatory guidance.

Why Are These Insights Valuable?
Inspire, motivate and be curious about the data and the possibilities it has for firefighting
Spot the potential opportunities.

These insights have many uses, including:

Data Analysis:

Customizing data as insightful visualisations
Directing future data collection
Extrapolating current trends

Designing for scale:
Designing with the next generation of drones and quadruped robots in mind.
Building for error-prone environments in the real world.
Informing feature engineering

Integration:
Informing human decision-making
New efforts without disrupting current and future fire fighting forces

Smart Action Planning:
Intelligently route the right alerts to swarms of robots operations
Customizable alerts are triggered or cleared at specific severity levels to help strategic planning.
Cross Border Cooperation in microgrids

Always Alert:
Proactive issue detection and prevention models.
Time analysis and alert propagation brings down fire alerts time from hours to seconds.

Ethical Concerns

Certainly, drone and robotic quadruped will need to play by the rules and follow regulatory guidelines, especially in emergency situations.

Evaluating, as Open Source recommends, a variety of issues including ethical concerns by contributors representing a wide range of viewpoints.

Wildfires are destructive forces, but they can occur naturally. Because of this, certain plants and animals have evolved to depend on periodic wildfires for ecological balance.
It might seem counterintuitive that a fire, which burns plant life and endangers animals within an ecosystem, could promote ecological health. But fire is a natural phenomenon, and nature has evolved with its presence.
Human-induced climate change promotes the conditions on which wildfires depend, increasing their likelihood -- according to a review of research on global climate change and wildfire risk.
In the end, it is true that the burden of preventing uncontrolled wildfires lies with humanity

FIRE IS CHAOS

Fire doesn’t care what it destroys or who it kills—it spreads without mercy, leaving total destruction in its wake.
We can't end wildfires altogether, but by better understanding their dynamics, ideally reducing impact from disasters.
Fire is its own element, but it’s influenced by a few key variables: wind, temperature, and humidity. The computer simulation is a place that allows researchers to tweak each of those, and fire extinguishing methods:
Cooling, starvation, smoothering, chain breaking mechanism or blanketing.
Perhaps using Lagrangian coherent structures (LCSs) as hotspots to activity directed at limiting the spread of fire and extinguishing (dropping fire retardant)

Not meant to be competitors with the firefighters, we want to be complementary
Because drones can fly day or night and gain rapid access to previously inaccessible urban or rural fires they can help to save both the lives of the public and first responders. Using intelligent robots to scout the area and drop water or Fire Retardant can allow humans to stand further back from the danger zone, only looking at the drones' data to make decisions from the safety of a command and control centre.

Firefighting automation is inevitable. It’s just a matter of time when tech will have the potential to alleviate challenges people face every day.

Cross Border Cooperation is a key element to be considered

The age of fire is upon us, climate change is subverting the system. The fires of this new era cannot always be tamed. Neither aircraft nor ground crews can do much for the blazes that spread quickly with powerful winds. That has forced firefighting “to be a global effort, not a state or national effort. Time to rethink the system of neighbours resource sharing.
Finding and identifying opportunitys, developing the projects for installation and operating the microgrids.

Precision Firefighting

Introduce the concept of Precision Firefighting for Early Site Specific Fire Management. As “a management strategy that uses information technology to bring data from multiple sources to bear on decisions associated with firefighting. Encompassing techniques and methods for firefighting management by taking into account their local and site-specific heterogeneity and variability.

A hackathon organizational rules facilitate broad participation, knowledge aggregation, and learning. would expand the range of possible solutions to the problems that algorithms create, enhance tech accountability, and foster public participation and learning.

To ensure continued development, it is necessary to encourage public participation. A key concern on development efforts associated with resource sharing. Built a software stack to support monitoring and management models that could get the initial developments off the ground.

We are looking forward to partnering and helping to grow this collective effort.

People who participated will at least have a better understanding of the problem. Data is just input. The insights people provide should be the most important output.


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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Open Dynamic Robot Initiative

An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research

 

https://open-dynamic-robot-initiative.github.io/

 

This website is the entry point to the ressources of the Open Dynamic Robot Initiative. This project originated in an effort to build a low cost and low complexity actuator module using brushless motors that can be used to build different types of torque controlled robots with mostly 3D printed and off-the-shelves components. This module, and extensions, can be used to build legged robots or manipulators. A paper describing the actuator module and the quadruped design can be found here.

 

https://open-dynamic-robot-initiative.github.io/solo_video_updated.mp4

 

Build you own robot!

All the hardware (drawings) and software has been open sourced under the BSD 3-clause license so the robots can easily be reproduced by other research laboratories. Everyone is welcomes to contribute to the project!

All the sources are hosted on the Open Dynamic Robot Initiative Github site

 

Partners

This work is done in collaboration between the Motion Generation and Control Group, the Dynamic Locomotion Group and the Robotics Central Scientific Facility at the Max-Planck Institute for Intelligent System , the Machines in Motion Laboratory at New York University's Tandon School of Engineering and the Gepetto Team at the LAAS/CNRS.

Referencing the project

You can reference the project with the following citation:

@article{grimminger2020open,
title={An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research},
author={F. {Grimminger} and A. {Meduri} and M. {Khadiv} and J. {Viereck} and M. {Wüthrich} and M. {Naveau} and V. {Berenz} and S. {Heim} and F. {Widmaier} and T. {Flayols} and J. {Fiene} and A. {Badri-Spröwitz} and L. {Righetti}},
journal={IEEE Robotics and Automation Letters},
year={2020},
volume={5},
number={2},
pages={3650-3657},
doi={10.1109/LRA.2020.2976639},
arXiv = {arXiv:1910.00093}}


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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

 

This paper describes an end-to-end pipeline for
tree diameter estimation based on semantic segmentation and
lidar odometry and mapping. Accurate mapping of this type of
environment is challenging since the ground and the trees are
surrounded by leaves, thorns and vines, and the sensor typically
experiences extreme motion. We propose a semantic feature based
pose optimization that simultaneously refines the tree models
while estimating the robot pose. The pipeline utilizes a custom
virtual reality tool for labeling 3D scans that is used to train a
semantic segmentation network. The masked point cloud is used
to compute a trellis graph that identifies individual instances and
extracts relevant features that are used by the SLAM module. We
show that traditional lidar and image based methods fail in the
forest environment on both Unmanned Aerial Vehicle (UAV) and
hand-carry systems, while our method is more robust, scalable,
and automatically generates tree diameter estimations.

 

Steven W. Chen, Guilherme V. Nardari, Elijah S. Lee, Chao Qu, Xu Liu, Roseli Ap. Francelin Romero, Vijay Kumar
IEEE Robotics and Automation Letters
Vol.: 5 Issue: 2
DOI: 10.1109/LRA.2019.2963823

 

 

 

 


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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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The ALERTWildfire network was designed to help firefighters identify nascent blazes and put them out.

Cover the entire state of California plus Nevada and Oregon.

 

http://www.alertwildfire.org/

This post was modified 5 months ago 2 times by forestfirehub

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Pybullet quadruped robot simulation. Code available.

 

https://www.youtube.com/watch?v=n7NX-Sw5sl4

 

Project details: https://hackaday.io/project/171456-diy-hobby-servos-quadruped-robot

 

Code is available at: https://github.com/miguelasd688/4-legged-robot-model

This post was modified 5 months ago 3 times by forestfirehub

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Microsoft open-sources its drone simulator

Microsoft's artificial-intelligence project offers developers a set of tools to train robots, drones, and other gadgets to operate autonomously.

The simulator may offer a way to generate data to train AI systems for autonomous vehicles.

A key part of the project is AirSim, which according to Microsoft offers a very realistic open-source simulator for flying and crashing drones, to generate data for training autonomous robots and other vehicles. The Unreal Engine-based simulator is available on GitHub for anyone to use.

 

https://github.com/Microsoft/AirSim

This post was modified 5 months ago by forestfirehub

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Python Simulation of Forest Fire in turtle graphics.

 

https://github.com/rafibarash/forest-fire

This post was modified 5 months ago by forestfirehub

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Fire Spread Model

 

Functions to simulate a forest fire with a simple spreading model

 

https://au.mathworks.com/matlabcentral/fileexchange/74499-fire-spread-model

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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A matlab toolbox for hydrocarbon fires modelization

 

fireSIM-Matlab-toolbox

 

https://au.mathworks.com/matlabcentral/fileexchange/72761-firesim-matlab-toolbox

 

https://github.com/YakNazim/fireSIM-Matlab-toolbox

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Air Dispersion Modeling

Software Solutions 

Testing Fire Extinguishers and Flame Retardants Dispersion

 

https://www.weblakes.com/products/air_dispersion.html

This post was modified 5 months ago by forestfirehub

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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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HRRR Smoke Model Tutorial

High Resolution Rapid Refresh smoke model

https://youtu.be/xQftyQjMeZ w"> https://youtu.be/xQftyQjMeZw

https://www.weather.gov/mfr/HRRR_smoke_tutorial


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forestfirehub
(@forestfirehub)
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Joined: 11 months ago
Posts: 44
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Openbot

Turning Smartphones into Robots

 

https://www.openbot.org/

https://github.com/intel-isl/OpenBot

https://youtu.be/qc8hFLyWDOM


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forestfirehub
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How aerial firefighters battle blazes from the skies

 

https://www.popsci.com/story/technology/aerial-firefighting-tankers-helicopters-california-wildfires/


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forestfirehub
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How do you fight extreme wildfires?

 

https://www.bbc.com/news/world-50410481


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forestfirehub
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The Forest Service and other federal, tribal, state, and local government agencies work together to respond to tens of thousands of wildfires annually.

 

https://www.fs.usda.gov/science-technology/fire/equipment-tools


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