PRE2023 3 Group10

Group members

Name	Student number	Email	Study	Responsibility
Dimitrios Adaos	1712926	d.adaos@student.tue.nl	Computer Science and Engineering	Simulation
Wiliam Dokov	1666037	w.w.dokov@student.tue.nl	Computer Science and Engineering	Design/hardware research
Kwan Wa Lam	1608681	k.w.lam@student.tue.nl	Psychology and Technology	Research/USE analysis
Kamiel Muller	1825941	k.a.muller@student.tue.nl	Chemical Engineering and Chemistry	Research/USE analysis
Georgi Nihrizov	1693395	g.nihrizov@student.tue.nl	Computer Science and Engineering	Simulation
Twan Verhagen	1832735	t.verhagen@student.tue.nl	Computer Science and Engineering	Design/hardware research

Introduction

Problem statement

Firefighting is a field where robotic technology can offer valuable assistance. The environment where human firefighters have to operate can be very harsh and challenging especially in closed spaces: low visibility due to smoke and lack of light, the presence of dangerous gases and substances, obstacles created by the fire that are not known a priori or change during the fire. In such scenarios, in order to help and save people that are trapped in a building and also to reduce the risks for the firefighters themselves, it is crucial to be able to determine the paths inside the building that are feasible to navigate and can lead to trapped or injured individuals.

Our group will focus on the design of a firefighting robot that is able to navigate inside a building, identify and avoid the fire sources and the obstacles that create impassible routes and assist firefighters in their search and rescue operations.

Objectives

Our objective is to design a robot that is able to operate inside a closed space to assist firefighters in their search and rescue operations.

We will target the most important features of such a robot:

• Detection and localization of fire sources and obstacles
• Detection of victims
• Discovery of feasible rescue paths
• Reliable communication
• Robust operation in an environment with low visibility and high temperatures

Users

Firefighters and first responders would be the primary users of the robot. These are the people that need to interact and deploy the robot in the first place. This means that the robot should be easy and quick to use and set up for in emergency situations where time is of the essence. It'd also be valuable to know their insights and experiences for the robot to work the most effectively in their field of expertise. It's also important that the robot can properly communicate with the firefighters in the emergency situation and relay the information about; fire sources, obstacles, victims, and feasible rescue paths.

The secondary user of a firefighting/rescue robot would be the victims and civilians. The robot is made to help them and come to their aid. It might be needed to find a way to communicate with the victims so they can be assisted most effectively. This might pose a challenge because of the low visibility and low audibility during a fire.

Interested parties for deploying the robot are firefighting authorities, that are tasked for responding to a fire incident and save lives and properties, insurance companies that can benefit from minimizing the loss of life and property and companies that own big buildings and can consider having the robot as part of their regular infrastructure.

Requirements

From the initial analysis of the literature the following list of features for the robot that would be beneficial to assisting firefighters in search and rescue missions has been identified:

• Ability to detect obstacles
• Ability to build a map of obstacles inside a building
• Ability to determine a path for reaching a specific place inside a building
• Ability to detect fire
• Ability to build a map of the fire inside the building
• Ability to operate in the presence of smoke, limited visibility and high temperatures
• Increased mobility (not too heavy and able to bypass small obstacles)
• Robust communication (ability to communicate the obstacle and fire maps to the firefighting teams)
• Ability to identify victims trapped inside a building
• Increased autonomy

After the interview with a firefighter from the firefighting station on the TU/E campus, a MoSCoW list (see user analysis) was constructed. This list was made taking both the user preferences and time constraints of this project into mind.

Approach

We will study existing firefighting robot solutions and related literature to identify detailed requirements and solutions to the challenges in the design of a firefighting robot. As well as consult actual firefighters about their opinions on requirements.

For the design of the main features of such robot we will evaluate their quality by using one of the available simulators (e.g. Netlogo, Webots, Gazebo, ROS, PyroSim)

Our target is to propose a robot design (HW and SW) that can be manufactured as a product to assist in firefighting tasks.

Planning

Gannt chart for initial project planning (Work in progress)

Research papers

№	Title	Labels	Summary
1	A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks	Victim detection, Convolutional neural network.	They trained a convolutional neural network to detect people and pets in thermal IR, images. They gathered their own dataset to train the network. The network results were pretty accurate (One-Step CNN 96.3%, Two-Step CNN 94.6%).
2	Early Warning Embedded System of Dangerous Temperature Using Single exponential smoothing for Firefighters Safety	Heat detection, Firefighter assistance.	Proposes to add a temperature sensor to a firefighter's suit which will warn firefighters that they are in a very hot place > 200 °C.
3	A method to accelerate the rescue of fire-stricken victims	Victim search method.	This paper describes an approach for locating victims and areas of danger in burning buildings. A floor plan of the burning building is translated into a grid so that the robot can navigate the building. A graph with nodes representing each of the rooms of the building is then generated from the grid to simplify the calculations needed for pathing. The algorithm used relies on crowdsourcing information normalized using fuzzy logic and the temperature of a region as detected by the thermal sensors of the robot to estimate the probability that a victim is present in a room. The authors of the paper found that their approach was significantly faster at locating survivors than strategies currently employed by firefighter and strategies devised by other researchers. Note: This paper uses the software PyroSim for their simulation. PyroSim offers a 30 day free trial, so it might be possible to use it for our own simulation. Needs further research into PyroSim.
4	The role of robots in firefighting	Overview current robots.	This paper describes the State of the Art in terrestrial and aerial robots for firefighting. At the same time the paper indicates that there is a general difficulty in the autonomy of such robots, mainly due to difficulties in visualizing the operation environment. There are, however, several projects aiming to address this issue and allow such robots to operate with more autonomy.
5	SLAM for Firefighting Robots: A Review of Potential Solutions to Environmental Issues	Simultaneous localization and mapping.	This paper aims to address some of the unfavorable conditions of fire scenes, like high temperatures, smoke, and a lack of a stable light source. It reviews solutions to similar problems in other fields and analyzes their characteristics from some previous  publications. Based on the analysis of this paper, to address the effect of smoke, a combination of laser based and radar based methods is considered more robust. For darkness effects, the combination of Laser based methods combined with image capture and processing is considered the best approach.  Thermal imaging technology is also suggested for addressing high temperatures.
6	A fire reconnaissance robot based on slam position, thermal imaging technologies, and AR display	Reconnaissance robot, Firefighter assistance, Thermal imaging, Simultaneous localization and mapping, Augmented reality.	Presents design of a fire reconnaissance robot (mainly focusing on fire inspection. Its function is on passing important fire information to fire fighters but not direct fire suppression) It can be used to assist the detection and rescuing processes under fire conditions. It adopts an infrared thermal image technology to detect the fire environment, uses SLAM (simultaneous localization and mapping)technology to construct the real-time map of the environment, and utilizes A* and D* mixed algorithms for path planning and obstacle avoidance. The obtained information such as videos are transferred simultaneously to an AR (Augmented Reality) goggle worn by the firefighters to ensure that they can focus on the rescue tasks by freeing their hands.
7	Design of intelligent fire-fighting robot based on multi-sensor fusion and experimental study on fire scene patrol	Firefighting robot, Path planning, Fire source detection, Thermal imaging, Binocular vision camera.	This paper presents the design of an intelligent Fire Fighting Robot based on multi-sensor fusion technology. The robot is capable of autonomous patrolling and fire-fighting functions. In this paper, the path planning and fire source identification functions are mainly studied, which are important aspects of robotic operation. A path-planning mechanism based on an improved version of the ACO(Ant Colony Optimization) is presented to solve that basic ACO is easy to converge in the local solution. It proposes a method to reduce the number of inflection points during movement to improve the motion and speed of the robot It uses a method for fire source detection, utilizing the combined operation of a binocular vision camera and and infrared thermal imager to detect and locate the fire source. It also uses ROS (Robot Operating System) based simulation to evaluate the algorithms for path planning.
8	Firefighting robot with deep learning and machine vision	Firefighting robot, Deep learning.	In this paper they made a fire fighting robot which is capable of extinguishing fires caused by electric appliances using a deep learning and machine vision. Fires are identified using a combination of AlexNet and ImageNet, resulting in a high accuracy (98.25% and 92% respectively).
9	An autonomous firefighting robot	Firefighting robot, Fire detection, Infrared sensor, Ultrasonic sensor.	They made an autonomous firefighting robot which used infrared and ultrasonic sensors to navigate and a flame sensor to detect fires.
10	Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning	Victim detection, Thermal imaging, Remote controlled.	A low resolution thermal camera is mounted on a remote controlled robot. The robot is trained to detect victims. The victim detection model has a moderately high detection rate of 75% in dense smoke.
11	Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics	Victim detection robot, multispectral imaging; primarily infrared and RGB	The effectiveness of three different cameras for victim detection. Namely a; RGB, thermal and multispectral camera.
12	Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot	Fire detection, Algorithm	Introduces an algorithm for a firefighting robot that finds fires using long wave infrared camera, ultraviolet radiation sensor and LIDAR.
13	On the Enhancement of Firefighting Robots using Path-Planning Algorithms	Path planning algorithm	Tests performance of several path-plannig algorithms to allow a firefighting robot to move more efficiently.
14	An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition	Autonomous firefighing robot, Deep learning algorithm	Made a firefighting robot that maps the area using an algorithm and uses a deep-learning-based flame detection technology utilizing a LIDAR.
15	Human Presence Detection using Ultra Wide Band Signal for Fire Extinguishing Robot	Victim detection, Remote control	A remotely controlled robot using ultra-wide band radar detects humans while fire and smoke are present based on the persons respiration movement.
16	Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Smoke	Real time object identification, Sensor fusion	Sensor fusion of stereo IR and FMCW radar was developed in order to improve the accuracy of object identification. This improvement ensures that the imagery shown is far more accurate while still maintaining real-time updates of the environment.
17	Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT Algorithm	Path planning algorithm	Introduces a bidirectional fast search algorithm based on violent matching and regression analysis. Violent matching allows for direct path search when there are few obstacles, the other segments ensure that the total path search is more efficient and less computationally heavy.
18	Round-robin study of a priori modelling predictions of the Dalmarnock Fire Test One	Fire modelling comparison	Compares the results of different types of fire simulation models, with a real-world experiment.
19	Summary of recommendations from the National Institute for Occupational Safety and Health Fire Fighter Fatality Investigation and Prevention Program	Most common causes of death of firefighters	Summary of the most common causes of death for firefighters. Cases were separated by nature and cause of death. They were also separated into 10 total categories as well 2 major categories - medical/trauma.
20	The current state and future outlook of rescue robotics	Overview current robots and what needs to be improved upon	Discusses the main requirements and challenges that need to be solved by search and rescue robots. Generally applicable to our firefighting robot. The most important aspects of search and rescue robots are: ease of use, autonomy, information gathering and use as tools.
21	Smart Fire Alarm System with Person Detection and Thermal Camera	Fire alarm, Person detection in fire, Heat detection	Discusses a smart system for fire alarms that distinguished between heat when people are present and when people aren't present.
22	See through smoke: robust indoor mapping with low-cost mmWave radar	Millimeter wave radar; Indoor mapping; Emergency response; Mobile robotics	Utilizing, a Generative adversarial neural network can reliably reconstruct a grid map of a room.
23	Analysis and design of human-robot swarm interaction in firefighting	Human- robot interaction, human robot swarm approach of firefighting, Existing robot human interaction in firefighting	Describes the cooperation between robots and firefighters during a firefighting mission, including mission planning and execution. The premise is that robots can add sensing capabilities to improve awareness and efficiency in obscured environments.
24	Using directional antennas as sensors to assist fire-fighting robots in large scale fires	Establish communications via robots, Disasters and firefighting	Describes how to establish communication networks between robots in disastrous fire situations using directional antennas so robots can be deployed to extinguish fires and reach places which firefighters can't easily reach..
25	Design And Implementation Of Autonomous Fire Fighting Robot	Fire prevention, Heat sensor, Extinguishing robot	Describes a robot that can be used to go into fires and reach places normal fire fighters would normally be unable to reach safely.
26	NL-based communication with firefighting robots	Overview existing robots, Communication robot-human, Natural language	Describes different methods of working together between firefighters and robots during fires and a robot that is meant for helping fire fighters during a fire
27	Experimental and computational study of smoke dynamics from multiple fire sources inside a large-volume building	Smoke dynamics, Statistical analysis, Smoke effects	Summarizes results from a fire simulation of 4 fire sources using the computational fluid dynamics code FDS (Fire Dynamics Simulator, v6.7.1) and compares those results to a single-source simulation, demonstrating the importance of the number of and position of fire sources in a simulation.
28	Numerical Analysis of Smoke Spreading in a Medium-High Building under Different Ventilation Conditions	Smoke dynamics, Statistical analysis	Uses simulation to compare smoke spreading in medium-high buildings under different ventilation conditions and draws conclusions on important points to consider in the design of a ventilation system for such buildings such as smoke inlets and outlets and high pressure zones.
29	A REVIEW OF RECENT RESEARCH IN INDOOR MODELLING & MAPPING	Indoor, Mapping, Modelling, Navigation	Summarizes the last 10 years of reasearch on indoor modelling and mapping. Describes a variety of used technologies, including lasers scanners, cameras and indoor data models such  as IFC, CityGML and IndoorGML. It also provides insight into recent navigation and routing algorithms with emphasis on dynamic environments.
30	Developing a simulator of a mobile indoor navigation application as a tool for cartographic research	Virtual model, Indoor navigation, Indoor mapping	Documents the process of creating of a proof of concept of a virtual indoor environment using Unreal Engine aimed at improving the indoor cartographic process. While still a prototype, the paper can be used to derive useful methods for building simulation and navigation.
31	Real time simulation of fire extinguishing scenarios		Describes software and methodology for simulating fire response scenarios. Demonstrates implementation of FDS with Unreal Engine to generate a fire scenario simulation.
32	Fire Fighting Mobile Robot: State of the Art and Recent Development	Overview current robots	This paper gives an overview of some of the state of the art fire fighting robots that are used today.

User analysis

The end goal for this project is to deliver a simulation of a remote controlled mapping robot that is capable of entering buildings that are on fire and provide mapping information regarding the layout of the building and where obstacles (including fire) and possibly victims are located.

The chosen programming environment for this task will be Unity.

For the realization for this simulation the following MoSCoW list has been constructed:

Must have:

• A simulation of a remote controlled firefighting robot in a fire scenario.
• Simulate the ability for a robot to turn the information from the 3D environment into a 2D map.
• Realistic fire and physics mechanics in the simulation.
• A simulation of the ability to detect fires and add them to the map (making a heat map).

Should have:

• A simulation of the ability to detect victims and add them to the map.
• A simulation of the ability to detect obstacles and add this information to the map.

Could have:

• The ability to communicate with or help victims.
• The ability to work (fully) autonomously.

Will not have:

• The ability to detect smoke and make a smoke map.
• The ability to open doors.
• The ability to rescue/extract victims.
• The ability to fight fires.
• The ability to of complex communication and interaction beyond just the data sharing.

Reasoning behind chosen objectives

For this project we wanted to deliver a proof of concept for (parts) of a mapping robot to be used by firefighters in buildings. We decided to do this by using a simulation for several reasons

• Our groups composition: as our group consists of 4 computer scientist, making a programmed simulation will cost less time learning new skills so we can focus more time on the proof of concept itself
• A simulation makes it easier to test & tweak the robot's software. As we don't have the resources to test in an actual burning building, a controlled fire in a lab is the best possible way to test a live robot. This still takes time to setup and is harder to repeatedly do. Testing a simulation model is easier, and so gives us more opportunity to test and tweak the robot more easily
• Making a simulation instead of a real model gives the benefit of being able to ignore the hardware side of the robot. When building a real model it must be made by using hardware and focus needs to be diverted to getting the right hardware in place. With a simulation we can focus on the parts of the robot we are going to give a proof of concept for.

We decided to make this simulation in Unity, some of the options we had and their advantages and disadvantages are listed in the next chapter of this wiki. We ultimately decided between using unity and ROS and chose unity for these reasons

• ROS can only be used on Linux, which makes installing it and setting it up and using it a lot more difficult and time expensive
• Unity has a lot more documentation and ready made code then ROS, so in unity we have more resources available to help us build the simulation
• Even though ROS has more features ready aimed at robot simulation, unity has all tools we need to build our simulation

For our simulation we made a MoSCoW list that shows what is going to be included in the simulation.

Simulator Selection

Main choices for simulation environments:

• Multi-agent frameworks: NetLogo, RePast
• ROS
• Unreal Engine
• FDS combined with an agent model
• FDS combined with ROS/Unreal

Discarded:

Swarm - outdated and inferior to NetLogo and RePast

NetLogo

Advantages:

• Simple and very versatile
• Manual control possible
• Well established in agent simulation

Disadvantages:

• No integrated fire/smoke simulation
• Cannot integrate actual sensor behavior

RePast

RePast (Recursive Porous Agent Simulation Toolkit) is an open-source agent-based modeling and simulation (ABMS) toolkit for Java. It is designed to support the construction of agent-based models.

Advantages:

• Can create very complex models
• Allows the use of Java libraries

Disadvantages:

• More complicated than Netlogo
• Bloated code (because Java)

ROS/RViz

RViz is a 3D visualization tool for ROS (Robot Operating System), which is commonly used in robotics research and development. ROS is an open-source framework for building robot software, providing various libraries and tools for tasks such as hardware abstraction, communication between processes, pathfinding, mapping and more.

Advantages:

• Can simulate sensor data
• Good 3D capabilities
• Well-established in professional robot development
• A variety of tools and plugins

Disadvantages:

• Fire/smoke simulation not supported - outside implementation needed

FDS + (Custom)agent simulator

Fire Dynamics Simulator (FDS) is a computational fluid dynamics (CFD) model of fire-driven fluid flow. FDS is a program that reads input parameters from a text file, computes a numerical solution to the governing equations, and writes user-specified output data to files.

FDS is primarily used to model smoke handling systems and sprinkler/detector activation studies, as well as for constructing residential and industrial fire reconstructions.

Advantages:

• Professional system; Well established in the industry
• Very accurate fire simulation
• 3D

Disadvantages:

• Pretty complicated to use
• Computationally expensive
• Couldn’t easily find resources for custom-agent behavior

Note:

PyroSym is a GUI which makes using FDS easy but it is paid unless a special offer is made for academic purposes

Unreal Engine + FDS

Data from FDS can be extracted and loaded into Unreal Engine and Unreal can handle agent simulation.

Advantages:

• Abundance of resources for Unreal Engine
• High flexibility
• Ease of use of Unreal Engine combined with accurate simulation from FDS
• Successful implementation in literature [31]

Disadvantages:

• Computationally expensive
• Data exporting might not be a simple process
• Complicated to use FDS

ROS/RViz + FDS

Advantages:

• Both well established professional softwares for their respective uses
• Very high control and customizability

Disadvantages:

• Did not find resources on successful implementation but no reason why it shouldn’t be possible.
• Both complicated and unknown softwares (might be too much work)
• Computationally expensive

State of the Art Robots

Because to this day firefighting is still a dangerous job, a lot of effort is put into the development of ways to minimize the risk for firefighters. In this age of technology this means that a lot of different fire fighting robots are being developed to try and help firefighters in this delicate task. To see what developments have already been made in this field we will now make an overview of current landscape of state of the art fire fighting robots. This will be done by first giving a couple of examples of a of robots that are being used today. This will paint a clear picture on what kind of technologies there are right now and what is already being done. Followed by our more general findings on the state of modern firefighting robots overall.

LUF60

Main Characteristics:

The LUF 60 fire fighting robot

Diesel powered, Fire extinguishing, Remote controlled

Description:

This is a remote controlled firefighting robot that is designed to distinguish fires. It consists of an air blower that blows a beam of water droplets (to a distance of approximately 60 meters). This water jet can go up to 2400 liters of water per minute. If needed it could also blow a beam of foam (to a distance of approximately 35 meters). It is designed to operate in difficult conditions and can, by remote control, be send directly to the fire source. To accomplish this the robot is able to remove obstacles and climb stairs (up to a 30 degree angle). It should be noted that LUF produces more robots similar to this one see.

Advantages:

• Minimizes risk for firefighters (remote controlled) and designed to also clear obstructions from a distance.
• Good extinguishing capabilities ( up to 2400 liters per minute).

Disadvantages:

• Its size exceeds that of a standard door restricting movement. Its dimensions are are 2.33m x 1.35m x 2.00-2.50m (length x width x height). A standard door is 0.80-0.90m x 2.00-2.10m (width x height).
• Its high weight of 2.200 kg, resulting in low mobility. The maximal speed is 4.5 km/h which is around the average walking speed.

EHang EH216-F

EHang EH216-F firefighting drone

Main Characteristics:

Battery powered, Fire extinguishing, Drone, Remote or directly controlled

Description:

This is a firefighting robot that can be either remote or manually controlled by a pilot. The main focus of this robot is to be able to extinguish fires in high-rise buildings that are not possible to extinguish from ground level, due to the limited range of fire hoses. The robot has a maximum flight altitude of 600 m, is able to carry a single person and has a maximum cruising speed of 130 km/h. The drone carries 100 l of firefighting liquid, with the spray lasting for 3.5 minutes and 6 fire extinguishing projectiles containing fire extinguishing powder and a window breaker. The drone is 7.33 m in length, 5.61 m in width and has a height of 2.2 m.

Advantages:

• Is able to reach and extinguish fires that grounded firefighting equipment cannot reach.
• Can extinguish fires from outside of the buildings, thus minimizing the risk for firefighters.
• Has very high mobility, meaning it can quickly respond to fires.

Disadvantages:

• Can only carry a set amount of fire extinguishing equipment, thus limiting it's capabilities.
• It's flight time is approximately 21 minutes, thus limiting deployment range and deployment time.

THERMITE RS3

The THERMITE RS3 firefighting robot

Main Characteristics:

Diesel powered, Fire extinguishing, Remote controlled

Description:

The THERMITE RS3 is a firefighting robot designed to extinguish fires from the outside of a building. It is a remote controlled diesel powered robot equipped with a water canon. It is capable of shooting a beam of water 100 meters horizontally and 50 meters vertically. With the capability of using foam if needed. It has also (relatively recently in 2020) been adopted into the Los Angeles Fire Department. It can also be noted that Howe & Howe produces more robots with a similar functionality as this one.

Advantages:

• Good extinguishing capabilities ( up to 9464 liters per minute).
• Good mobility for its size (up to 13 km/h and ability to climb 35% slope while weighing 1588 kg).

Disadvantages:

• The robot was not designed to go inside of buildings so it has limited adaptability. Its dimensions are are 2.14m x 1.66m x 1.63m (length x width x height). A standard door is 0.80-0.90m x 2.00-2.10m (width x height).

COLOSSUS

The COLOSSUS fire fighting robot

Main Characteristics:

Battery powered, Fire extinguishing, Reconnaissance, Remote controlled

Description:

The COLOSSUS is a firefighting robot developed to fight fires in indoors and outdoors situations. It is an remotely controlled robot with some AI integration that provides driving assistance. The main purpose of this robot is to extinguish fires in places to dangerous for firefighters to go. The robot is battery powered (batteries last up to 12 hours) and has 9 interchangeable models that can be used for different situations. Because of these different models it is very adaptable and can be used from reconnaissance to victim extraction. A very notable instance of the robot being used was when they were deployed alongside the Paris firefighters during the Notre-Dame fire in 2019. Where they were used to cool down the insides of the cathedral where firefighters couldn't go because of falling debris. Now the robot is being used in 15 different countries. Shark robotics produces more similar robots.

Advantages:

• Relatively long operation time despite being battery powered (up to 12 hours in operational situations).
• Capable to withstand high temperatures (also waterproof and dustproof).
• Can be deployed out door and indoor (minimizes risk for firefighters). The robots dimensions are 1.60m x 0.78m x 0.76m (length x width x height). Can also climb slopes of up to 40 degrees.
• Different mountable modules for different situations (for instance 180 degree video turret or wounded people transport stretcher)
• Good extinguishing capabilities (3000 liters per minute)

Disadvantages:

• The robot has an extremely low speed of up to 3.5 km/h (which is slower then average walking speed).

X20 Quadruped Robot Dog

The X20 Quadruped Robot Dog

Main Characteristics:

Battery powered, Reconnaissance, Autonomous and Remote controlled, Quadruped

Description:

The X20 Quadruped Robot Dog is a fully autonomous robot (with remote control capabilities) made to traverse difficult terrain such as ruins, piles of rubble and other complex terrains using its four legs. Furthermore the robot was developed to detect hazards using its different sensors and cameras. It comes equipped with a bi-spectrum PTZ camera, a dynamic infrared camera, gas sensor, sound pickup and LiDAR. Using a SLAM algorithm it gets the measurements of its environment and builds a 3D map of it. It also comes equipped with a light weighted robotic arm. The main job of this robot in a disaster area is to do reconnaissance which is directly send back to the digital system. It can collect sound from victims that it finds and make calls with them. Also calculating the best pathways to get to safety. Deep Robotics has produced more similar robots and even a newer model (the X30) however the X20 was specifically marketed towards rescue operations as to why we chose to look further into this older model (2021).

Advantages:

• The robot is relatively light (53kg) and can go up to 15 km/h*.
• The robot can traverse difficult terrain (its able to traverse 20cm high obstacles and climb stairs and 30 degree slopes) and is also capable of operating indoors as well as outdoors. The robots dimensions are 0.95m x 0.47m x 0.70m (length x width x height).
• The robot is fully autonomous an can make a mapping of the area as well ass detect temperatures, toxic gases and victims. Making it a good reconnaissance robot and making the job more safe for firefighters.

Disadvantages:

• The robot is battery powered and only lasts 2-4 hours.
• Even though it can work under extreme conditions such as downpour, dust storm, frigid temperatures and hail. It wasn't specified whether it could work under extreme heat.

*It should be noted that some specifications of the robot varied pretty significantly on the manufacturers own website.

SkyRanger R70

The SkyRanger R70

Main Characteristics:

Battery powered, Drone, Reconnaissance, Remote controlled, Semi-autonomous

Description:

The SkyRanger R70 comes was developed for a wide range of missions including but not restricted to fire scenes and search and rescue operations. The main goal of the drone is to give an unobstructed wider view of what is happening in such a situation. It comes equipped with a detailed thermal camera making it ideal for analyzing fires from a safe distance. The drone also carries a computer making it capable of using AI for object detection and classification. It isn't specified but it is implied that the drone is only meant for outside use meaning that no scouting inside of buildings can be done with this robot. Teledyne FLIR makes more similar drones, thermal cameras and other products.

Advantages:

• Being a drone this robot is very mobile, with top speeds of 50 km/h.
• Can give a very detailed top down view, including thermal vision.

Disadvantages:

• The drone is only meant for reconnaissance meaning that it can only carry and deliver payloads up to 2 kg.
• Its battery only lasts up to 50 minutes.
• Can only operate in temperatures up to 50 degrees Celsius, meaning that it has to keep a safe distance from the fire.

Appendix

Appendix 1; Logbook

Logbook

Week	Name	Hours spent	Total hours
1	Dimitrios Adaos	Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers (2h), Read and summarize papers (8h) Wrote Introduction (6h)	19.5h
	Wiliam Dokov	Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers (3h), Summarry (7h)	13.5h
	Kwan Wa Lam	Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers(1h), Read and summarize papers (7h)	11.5h
	Kamiel Muller	Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers(1h)
	Georgi Nihrizov	Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers(2h), Read and summarize papers (8h)	13.5h
	Twan Verhagen	Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers (1h)	4,5h
2	Dimitrios Adaos	Weekly evaluation (0.5h), Meeting (2h), Meeting (1h), Interview with firefighter (2h), Processing interview questions (2h)	7.5h
	Wiliam Dokov	Weekly evaluation (0.5h), Meeting (2h), Meeting (1h), Interview with firefighter (2h), Researching simulation options (1h)	6.5h
	Kwan Wa Lam	Meeting (1h), Work on Wiki page (2h), Literature Research (3h), User Analysis (1.5h), Reviewing Wiki(1h)	8.5h
	Kamiel Muller	Weekly evaluation (0.5h), Meeting (2h), Correspondence firefighting station (0.5h), Meeting (1h), Work on Wiki page (2h)
	Georgi Nihrizov	Weekly evaluation (0.5h), Meeting (2h), Meeting (1h), Research Simulation Environments (8h)	11.5h
	Twan Verhagen	Weekly evaluation (0.5h), Meeting (2h), Meeting (1h), Reviewing Wiki(1h), Researching Literature(3h)	7,5h
3	Dimitrios Adaos	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h), Experimenting with Unreal Engine 5 (2h), Installing FDS and converting the data from it to a usable format (6h), Installing ROS and RViz (Windows) (0.5h)	12.5h
	Wiliam Dokov	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h), Installing Ros (On windows) (1h), Researching viable Ros + Gazebo simulation methods for windows (2h), Trying to implement Gazebo simulation on Windows (2h), Doing ROS basic tutorial (2h) Doing Gazeebo basic tutorial and figuring out how plugins work (3h)	14h
	Kwan Wa Lam	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h), Researching Literature(6h), Adding Literature to Wiki(4h)	14h
	Kamiel Muller	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h), Researching Literature(5h), Adding Literature to Wiki and updating segments introduction of Wiki (4h)	13 h
	Georgi Nihrizov	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h), FDS research (8h)	12h
	Twan Verhagen	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h), adding labels to wiki(2h), research into sensors (3h)	9h
4	Dimitrios Adaos	Weekly evaluation (0.5h),Discussion (1h), Thursday Meeting (1h), Familiarization (Watching tutorials etc.) with Unity (4h), Python programming for translation of FDS data (2h), Programming in Unity (4h)	12.5h
	Wiliam Dokov	Weekly evaluation (0.5h),Discussion (1h), Thursday Meeting (1h), Setting up Basic scene in unity (1h), Setting up basic robot (3h), Setting up controls (3h), Setting up Lidar (6h)	15.5
	Kwan Wa Lam	Weekly evaluation (0.5h), Discussion (1h), Thursday Meeting (1h), Setting up and learning Unity (5h), Finding a building plan and making virtual environment (4h)	11.5h
	Kamiel Muller	Weekly evaluation (0.5h), Discussion (1h), Thursday Meeting (1h), Setting up Unity and getting acquainted with it (5h), Making a start on making robot movement (3h)	10.5 h
	Georgi Nihrizov	Weekly evaluation (0.5h), Discussion (1h), Heat Sensor Research (4h), Heat Sensor simulation (6h)	11.5h
	Twan Verhagen	Weekly evaluation(0,5h), discussion(1h), Thursday meeting (1h), setting up the correct version of unity and exploring it(5h), exploring minimaps and ways to show results on it on unity(4h)	11,5h
5	Dimitrios Adaos	Weekly evaluation (0.5h), Discussion (2h), Sunday Meeting (1h)
	Wiliam Dokov	Weekly evaluation (0.5h), Discussion (2h), Sunday Meeting (1h)
	Kwan Wa Lam	Weekly evaluation (0.5h), Discussion (2h), Sunday Meeting (1h)
	Kamiel Muller	Weekly evaluation (0.5h), Discussion (2h), Sunday Meeting (1h)
	Georgi Nihrizov	Weekly evaluation (0.5h), Discussion (2h), Sunday Meeting (1h)
	Twan Verhagen	Weekly evaluation (0,5h), Discussion(2h), Research into creating a map and testing in unity(6h), adding reasoning to the wiki(2h)	10,5h
6	Dimitrios Adaos
	Wiliam Dokov
	Kwan Wa Lam
	Kamiel Muller
	Georgi Nihrizov
	Twan Verhagen

Appendix 2; Firefighter Interview

Question: What does a typical firefighting mission look like? How do you gather information? Do you work in a team? What are your objectives and priorities (Search for people, extinguish fire)?

Answer: We usually walk around the building to find the location of the fire and make sure that the fire does not spread. We also make sure that all doors and windows are closed to prevent any air currents from causing a back-draft that leads to an explosion. We then determine if we can take an aggressive approach to extinguishing the fire; by entering the building, or a passive/defensive approach; by trying to extinguish from outside. When we do enter the building our first priority is looking for people, then extinguishing. We do this to avoid risking that people will inhale the smoke from extinguishing the flames. The most important things to know are the characteristics of the building in which the fire is located, and to find out this information we usually ask the people in the area. We also generally need to know how much water the fire we are trying to extinguish needs. Generally 1 couch needs 1 hose of water to extinguish.

Question: What usually goes wrong during these missions? Answer: We use portable phones for communication inside the buildings and so communication can be an issue. It is also difficult to find out which way we need to go due to smoke/visibility issues.

Question: What are the main causes of failures during a firefighting mission?

Answer: Flammable materials that can cause explosions are the most dangerous and we need to make sure we are nowhere near them when we discover them. There is also the danger of running out of water to extinguish, at which point we have to give up on extinguishing the fire. Also, for metal and concrete buildings that contract due to high temperatures are likely to collapse after about an hour of being aflame.

Question: Do you have any ideas on how to prevent these issues?

Answer: When we encounter containers of flammable materials we try to remove them from the building while cooling them with water. We then place them behind walls to keep ourselves and others safe. If we know that a metal or concrete building has been burning for a while then we simply do not enter and try to passively/defensively extinguish the fire.

Question: What are the current firefighting tools at your disposal? Do you think that you need something more?

Answer: We have an infrared camera, a CO₂ meter to detect dangerous substances, oxygen masks and some tools for opening doors safely.

Question: In the ideal case what functions would you want the robot to perform?

Answer: Most important would be information about heat/a heatmap and information about where people are located inside burning buildings. We would then need information about the structure of the building, how big the fire is and if possible the location of any obstacles inside the building.

Question: What level of autonomy would be best? With autonomy defined as what kinds of permissions the robot has to do without human intervention.

Answer: For us the robot should be able to work on its own, but have a simple interface so that our chief/director can direct it from a tablet in our firetruck if needed.

Question: What information are you missing and would like to know when there is a fire in a building?

Answer: In order of priority:

1. Information about people in the building
2. Heatmap
3. Smoke map
4. Basic obstacle map

Question: Suppose there is a hard-to-reach or inaccessible area, how influential would it be if a robot would be able to reach it fairly easily?

Answer: This really depends more on the area, how many people are inside, if there is a fire in that location, or maybe any dangerous/explosive substances.

Question: Have you used robots during your work. If yes what was your experience with them? Have you had any issues with them?

Answer: I have not used any robots in my firefighting career.

Question: Rank the following features based on importance (omitted here since they are present in the answer)

Answer: In order of priority (with an addendum for some features at the end of the answer)

1. Heat resistance
2. Ability to find people
3. Clarity of sensory picture
4. Mobility

• Level of autonomy: Answer: Needs to be autonomous but with the capacity to direct it if needed
• Speed: Answer: Depends on size of building, most buildings are not that large
• Accuracy: Answer: relevant for larger scale (need to distinguish 200°C from 400°C), but high accuracy not important at smaller scale (160°C vs 180°C)

Question: Do you have something more to add on this topic? Answer: Finding an entrance point for the robot could be dangerous, we can't really open any doors or windows easily to let the robot go in due to the risk of a back-draft.

Question: How long does a fire take to extinguish for an average house?

Answer: For normal houses the entire process of extinguishing a fire takes about 10-15 minutes.