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 can prevent navigation 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 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

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.

User analysis

Here we will do an analysis using the MoSCoW method to get a grasp of the requirements of the robot as well as a clear overview of what we need the robot to do and what has to be prioritized.

Must have:

• A simulation of the firefighting robot in a fire scenario.
• Simulate a mapping algorithm into a 2d map (to update a given floorplan).
• 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).
• A simulation of the ability to determine a path for reaching a specific place inside a building.
• The ability to communicate the acquired information with the fire control center on site (its map, obstacles, fires).
• The ability to respond in 'real time' to orders given in the simulation, including exploring without human guidance.

Should have:

• The ability to detect victims and add them to the map and simulate it.
• A simulation of the ability to detect obstacles and add this information to the map that it's working with.
• Good mobility and the ability to bypass small obstacles and traverse difficult environments (it shouldn't be too heavy for instance).
• Durability and the ability to operate in the conditions of a fire, including but not limited to; presence of smoke, limited visibility and high temperatures.

Could have:

• The ability to traverse stairs.
• Simulate a mapping algorithm into a 2d map (without a given floorplan).
• Simulate different path finding algorithms.
• The ability to detect fire sources and add them to the map (making a heat map).
• The ability to communicate with or help victims.

Will not have:

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

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

(Insert robot name)

(Insert Picture)

Description: ...

Advantages/Disadvantages: ...

(Insert robot name)

(Insert Picture)

Description: ...

Advantages/Disadvantages: ...

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)
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),
	Kwan Wa Lam	Meeting (1h), Work on Wiki page (2h), Literature Research (3h), User Analysis (1h), Reviewing Wiki(1h)
	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)
3	Dimitrios Adaos	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h),
	Wiliam Dokov	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h),
	Kwan Wa Lam	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h),
	Kamiel Muller	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h),
	Georgi Nihrizov	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h),
	Twan Verhagen	Weekly evaluation (0.5h), Meeting (2h), Meeting (1.5h),
4	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.

Sources backup (temporary)

1. Jaradat, F. B., & Valles, D. (2020). A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks. 2020 10th Annual Computing And Communication Workshop And Conference (CCWC). https://doi.org/10.1109/ccwc47524.2020.9031275
2. Jaradat, F., & Valles, D. (2018). Early Warning Embedded System of Dangerous Temperature Using Single exponential smoothing for Firefighters. . . ResearchGate. https://www.researchgate.net/publication/327209779_Early_Warning_Embedded_System_of_Dangerous_Temperature_Using_Single_exponential_smoothing_for_Firefighters_Safety
3. Lin, Z., & Tsai, P. (2024). A method to accelerate the rescue of fire-stricken victims. Expert Systems With Applications, 238, 122186. https://doi.org/10.1016/j.eswa.2023.122186
4. Bogue, R. (2021). The role of robots in firefighting. Industrial Robot-an International Journal, 48(2), 174–178. https://doi.org/10.1108/ir-10-2020-0222
5. Hong, Y. (2022). SLAM for Firefighting Robots: A Review of Potential Solutions to Environmental Issues. 2022 5th World Conference On Mechanical Engineering And Intelligent Manufacturing (WCMEIM). https://doi.org/10.1109/wcmeim56910.2022.10021457
6. Li, S., Feng, C., Niu, Y., Shi, L., Wu, Z., & Song, H. W. (2019). A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display. Sensors, 19(22), 5036. https://doi.org/10.3390/s19225036
7. Zhang, S., Yao, J., Wang, R., Liu, Z., Ma, C., Wang, Y., & Zhao, Y. (2022). Design of intelligent fire-fighting robot based on multi-sensor fusion and experimental study on fire scene patrol. Robotics And Autonomous Systems, 154, 104122. https://doi.org/10.1016/j.robot.2022.104122
8. Dhiman, A., Shah, N., Adhikari, P., Kumbhar, S., Dhanjal, I. S., & Mehendale, N. (2021). Firefighting robot with deep learning and machine vision. Neural Computing And Applications, 34(4), 2831–2839. https://doi.org/10.1007/s00521-021-06537-y
9. Hassanein, A., Elhawary, M., Jaber, N., & El-Abd, M. (2015). An autonomous firefighting robot. IEEE. https://doi.org/10.1109/icar.2015.7251507
10. Gelfert, S. (2023). Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning. In Lecture notes in networks and systems (pp. 351–364). https://doi.org/10.1007/978-3-031-26889-2_32
11. Ulloa, C. C., Orbea, D., Del Cerro, J., & Barrientos, A. (2024). Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics. Applied Sciences, 14(2), 766. https://doi.org/10.3390/app14020766
12. Kim, J., Keller, B., & Lattimer, B. Y. (2013). Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot. IEEE. https://doi.org/10.1109/aim.2013.6584304
13. Ramasubramanian, S., & Muthukumaraswamy, S. A. (2021). On the Enhancement of Firefighting Robots using Path-Planning Algorithms. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00578-9
14. Li, S., Yun, J., Feng, C., Gao, Y., Yang, J., Sun, G., & Zhang, D. (2023). An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition. Fire, 6(3), 93. https://doi.org/10.3390/fire6030093
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