PRE2023 3 Group10: Difference between revisions

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* The ability to build a comprehensive map of the inside layout of a building.
* The ability to build a comprehensive map of the inside layout of a building.
* The ability to detect obstacles and add this information to the map that it's working with.
* The ability to detect obstacles and add this information to the map that it's working with.
* The ability to detect fires and add them to the map.
* The ability to detect victims trapped inside the building and add them to the map.
* The ability to determine a path for reaching a specific place inside a building.
* The ability to determine a path for reaching a specific place inside a building.
* The ability to detect fires and add them to the map.
*The ability to communicate the acquired information with the firefighters (its map, obstacles, fires, detected victims)
*
* A certain level of autonomy.
*
*
*
* Robust communication (ability to communicate the obstacle and fire maps to the firefighting teams)
* Ability to identify victims trapped inside a building
* Increased autonomy


=== Should have: ===
=== Should have: ===


* Good mobility and the ability to bypass small obstacles (it shouldn't be too heavy).
* Good mobility and the ability to bypass small obstacles (it shouldn't be too heavy).
* The ability to communicate with or help victims.


=== Could have: ===
=== Could have: ===


* The ability to traverse stairs.
* The ability to traverse stairs.
* The ability to open doors.


=== Will not have: ===
=== Will not have: ===

Revision as of 16:27, 24 February 2024

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[1] 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%).[1]
2 Early Warning Embedded System of Dangerous Temperature Using Single exponential smoothing for Firefighters Safety[2] 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.[2]
3 A method to accelerate the rescue of fire-stricken victims[3] 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.[3]

4 The role of robots in firefighting[4] 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.[4]
5 SLAM for Firefighting Robots: A Review of Potential Solutions to Environmental Issues[5] 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.[5]

6 A fire reconnaissance robot based on slam position, thermal imaging technologies, and AR display[6] 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.[6]
7 Design of intelligent fire-fighting robot based on multi-sensor fusion and experimental study on fire scene patrol[7] 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.[7]

8 Firefighting robot with deep learning and machine vision[8] 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).[8]
9 An autonomous firefighting robot[9] 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.[9]
10 Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning[10] 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.[10]
11 Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics[11] The effectiveness of three different cameras for victim detection. Namely a; RGB, thermal and multispectral camera.[11]
12 Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot[12] Introduces an algorithm for a firefighting robot that finds fires using long wave infrared camera, ultraviolet radiation sensor and LIDAR.[12]
13 On the Enhancement of Firefighting Robots using Path-Planning Algorithms[13] Tests performance of several path-plannig algorithms to allow a firefighting robot to move more efficiently.[13]
14 An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition[14] Made a firefighting robot that maps the area using an algorithm and uses a deep-learning-based flame detection technology utilizing a LIDAR.[14]
15 Human Presence Detection using Ultra Wide Band Signal for Fire Extinguishing Robot[15] A remotely controlled robot using ultra-wide band radar detects humans while fire and smoke are present based on the persons respiration movement.[15]
16 Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Smoke[16] 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.[16]
17 Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT Algorithm[17] 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.[17]
18 Round-robin study of a priori modelling predictions of the Dalmarnock Fire Test One[18] Compares the results of different types of fire simulation models, with a real-world experiment.[18]
19 Summary of recommendations from the National Institute for Occupational Safety and Health Fire Fighter Fatality Investigation and Prevention Program[19] 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.[19]
20 The current state and future outlook of rescue robotics[20] 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.[20]
21 Smart Fire Alarm System with Person Detection and Thermal Camera[21] Discusses a smart system for fire alarms that distinguished between heat when people are present and when people aren't present.[21]
22 See through smoke: robust indoor mapping with low-cost mmWave radar[22] Utilizing, a Generative adversarial neural network can reliably reconstruct a grid map of a room.[22]
23 Analysis and design of human-robot swarm interaction in firefighting[23] 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.[23]
24 Using directional antennas as sensors to assist fire-fighting robots in large scale fires[24] 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..[24]
25 Design And Implementation Of Autonomous Fire Fighting Robot[25] Describes a robot that can be used to go into fires and reach places normal fire fighters would normally be unable to reach safely.[25]
26 NL-based communication with firefighting robots[26] 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[26]
27 Experimental and computational study of smoke dynamics from multiple fire sources inside a large-volume building[27] 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.[27]
28 Numerical Analysis of Smoke Spreading in a Medium-High Building under Different Ventilation Conditions[28] 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.[28]
29 A REVIEW OF RECENT RESEARCH IN INDOOR MODELLING & MAPPING[29] 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.[29]
30 Developing a simulator of a mobile indoor navigation application as a tool for cartographic research[30] 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.[30]

User analysis

Here we will do an analysis using the MoSCoW method to get a grasp of the requirements of the

Must have:

  • The ability to operate in the conditions of a fire, including but not limited to; presence of smoke, limited visibility and high temperatures.
  • The ability to build a comprehensive map of the inside layout of a building.
  • The ability to detect obstacles and add this information to the map that it's working with.
  • The ability to detect fires and add them to the map.
  • The ability to detect victims trapped inside the building and add them to the map.
  • The ability to determine a path for reaching a specific place inside a building.
  • The ability to communicate the acquired information with the firefighters (its map, obstacles, fires, detected victims)
  • A certain level of autonomy.

Should have:

  • Good mobility and the ability to bypass small obstacles (it shouldn't be too heavy).
  • The ability to communicate with or help victims.

Could have:

  • The ability to traverse stairs.
  • The ability to open doors.

Will not have:

  • The ability to work fully autonomously.
  • The ability to rescue victims.
  • The ability to fight fires.

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)

13.5
Twan Verhagen Introductory Lecture (2h), Meeting (1h), Brainstorm (0.5h), Find papers (1h)
2 Dimitrios Adaos Meeting (1h),
Wiliam Dokov Meeting (1h),
Kwan Wa Lam Meeting (1h), Work on Wiki page (2h)
Kamiel Muller Meeting (1h),
Georgi Nihrizov Meeting (1h),
Twan Verhagen Meeting (1h),
3 Dimitrios Adaos
Wiliam Dokov
Kwan Wa Lam
Kamiel Muller
Georgi Nihrizov
Twan Verhagen
4 Dimitrios Adaos
Wiliam Dokov
Kwan Wa Lam
Kamiel Muller
Georgi Nihrizov
Twan Verhagen

Sources

  1. ↑ 1.0 1.1 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. ↑ 2.0 2.1 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. ↑ 3.0 3.1 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. ↑ 4.0 4.1 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. ↑ 5.0 5.1 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. ↑ 6.0 6.1 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. ↑ 7.0 7.1 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. ↑ 8.0 8.1 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. ↑ 9.0 9.1 Hassanein, A., Elhawary, M., Jaber, N., & El-Abd, M. (2015). An autonomous firefighting robot. IEEE. https://doi.org/10.1109/icar.2015.7251507
  10. ↑ 10.0 10.1 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. ↑ 11.0 11.1 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. ↑ 12.0 12.1 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. ↑ 13.0 13.1 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. ↑ 14.0 14.1 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
  15. ↑ 15.0 15.1 Bandala, A. A., Sybingco, E., Maningo, J. M. Z., Dadios, E. P., Isidro, G. I., Jurilla, R. D., & Lai, C. (2020). Human Presence Detection using Ultra Wide Band Signal for Fire Extinguishing Robot. IEEE. https://doi.org/10.1109/tencon50793.2020.9293893
  16. ↑ 16.0 16.1 Kim, J., Starr, J. W., & Lattimer, B. Y. (2014). Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Smoke. Fire Technology, 51(4), 823–845. https://doi.org/10.1007/s10694-014-0413-6
  17. ↑ 17.0 17.1 Tong, T., Guo, F., Wu, X., Dong, H., Liu, O., & Yu, L. (2021). Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT Algorithm*. IEEE. https://doi.org/10.1109/iciea51954.2021.9516153
  18. ↑ 18.0 18.1 Rein, G., Torero, J. L., Jahn, W., Stern-Gottfried, J., Ryder, N. L., Desanghere, S., Lázaro, M., Mowrer, F. W., Coles, A., Joyeux, D., Alvear, D., Capote, J., Jowsey, A., Abecassis-Empis, C., & Reszka, P. (2009). Round-robin study of a priori modelling predictions of the Dalmarnock Fire Test One. Fire Safety Journal, 44(4), 590–602. https://doi.org/10.1016/j.firesaf.2008.12.008
  19. ↑ 19.0 19.1 Hard, D. L., Marsh, S. M., Merinar, T. R., Bowyer, M. E., Miles, S. T., Loflin, M. E., & Moore, P. W. (2019). Summary of recommendations from the National Institute for Occupational Safety and Health Fire Fighter Fatality Investigation and Prevention Program, 2006–2014. Journal Of Safety Research, 68, 21–25. https://doi.org/10.1016/j.jsr.2018.10.013
  20. ↑ 20.0 20.1 Delmerico, J. A., Mintchev, S., Giusti, A., Gromov, B., Melo, K., Havaš, L., Cadena, C., Hutter, M., Ijspeert, A. J., Floreano, D., Gambardella, L. M., Siegwart, R., & Scaramuzza, D. (2019). The current state and future outlook of rescue robotics. Journal Of Field Robotics, 36(7), 1171–1191. https://doi.org/10.1002/rob.21887
  21. ↑ 21.0 21.1 Ma, Y., Feng, X., Jiao, J., Peng, Z., Qian, S., Xue, H., & Li, H. (2020). Smart Fire Alarm System with Person Detection and Thermal Camera. In Lecture Notes in Computer Science (pp. 353–366). https://doi.org/10.1007/978-3-030-50436-6_26
  22. ↑ 22.0 22.1 Lu, C. X., La Rosa, S., Zhao, P., Wang, B., Chen, C., Stankovic, J. A., Trigoni, N., & Markham, A. (2019). See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1911.00398
  23. ↑ 23.0 23.1 Naghsh, A. M., Gancet, J., Tanoto, A., & Roast, C. (2008). Analysis and design of human-robot swarm interaction in firefighting. IEEE. https://doi.org/10.1109/roman.2008.4600675
  24. ↑ 24.0 24.1 Min, B., Matson, E. T., Smith, A., & Dietz, J. E. (2014). Using directional antennas as sensors to assist fire-fighting robots in large scale fires. IEEE. https://doi.org/10.1109/sas.2014.6798976
  25. ↑ 25.0 25.1 Reddy, M. S. (2021). Design and implementation of autonomous fire fighting robot. Turkish Journal Of Computer And Mathematics Education (TURCOMAT), 12(12), 2437–2441. https://turcomat.org/index.php/turkbilmat/article/view/7836
  26. ↑ 26.0 26.1 Hong, J. H., Min, B., Taylor, J. M., Raskin, V., & Matson, E. T. (2012). NL-based communication with firefighting robots. IEEE. https://doi.org/10.1109/icsmc.2012.6377941
  27. ↑ 27.0 27.1 Vigne, G., Węgrzyński, W., Cantizano, A., Ayala, P., Rein, G., & Gutiérrez-Montes, C. (2020). Experimental and computational study of smoke dynamics from multiple fire sources inside a large-volume building. Building Simulation, 14(4), 1147–1161. https://doi.org/10.1007/s12273-020-0715-1
  28. ↑ 28.0 28.1 Salamonowicz, Z., Majder–Łopatka, M., Dmochowska, A., Piechota-Polańczyk, A., & Polańczyk, A. (2021). Numerical Analysis of Smoke Spreading in a Medium-High Building under Different Ventilation Conditions. Atmosphere, 12(6), 705. https://doi.org/10.3390/atmos12060705
  29. ↑ 29.0 29.1 Gündüz, M. Z., Işıkdağ, Ü., & Başaraner, M. (2016). A REVIEW OF RECENT RESEARCH IN INDOOR MODELLING & MAPPING. The International Archives Of The Photogrammetry, Remote Sensing And Spatial Information Sciences, XLI-B4, 289–294. https://doi.org/10.5194/isprs-archives-xli-b4-289-2016
  30. ↑ 30.0 30.1 Łobodecki, J., & Gotlib, D. (2022). Developing a simulator of a mobile indoor navigation application as a tool for cartographic research. Polish Cartographical Review, 54(1), 108–122. https://doi.org/10.2478/pcr-2022-0008

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
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