PRE2023 3 Group10

Robot for saving victims in a fire

Group members

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

Research papers

1. A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks https://ieeexplore.ieee.org/abstract/document/9031275

Title	Summary	Link
A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks	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.	https://ieeexplore.ieee.org/abstract/document/9031275
Early Warning Embedded System of Dangerous Temperature Using Single exponential smoothing for Firefighters Safety	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.	https://shorturl.at/bcGJ2
A method to accelerate the rescue of fire-stricken victims Zheng-Ting Lin, Pei-Hsuan Tsai, Expert Systems with Applications, Volume 238, Part E, 2024, 122186, ISSN 0957-4174		https://www.sciencedirect.com/science/article/pii/S095741742302688X
The role of robots in firefighting Bogue, R. (2021), Industrial Robot, Vol. 48 No. 2, pp. 174-178.		https://www.emerald.com/insight/content/doi/10.1108/IR-10-2020-0222/full/html
Evaluation of a Sensor System for Detecting HumansTrapped under Rubble: A Pilot Study Zhang D, Sessa S, Kasai R, Cosentino S, Giacomo C, Mochida Y, Yamada H, Guarnieri M, Takanishi A. Sensors. 2018; 18(3):852.		https://doi.org/10.3390/s18030852
A fire reconnaissance robot based on slam position, thermal imaging technologies, and AR display Li S, Feng C, Niu Y, Shi L, Wu Z, Song H. Sensors. 2019; 19(22):5036.		https://doi.org/10.3390/s19225036
Design of intelligent fire-fighting robot based on multi-sensor fusion and experimental study on fire scene patrol Shuo Zhang, Jiantao Yao, Ruochao Wang, Zisheng Liu, Chenhao Ma, Yingbin Wang, Yongsheng Zhao, Robotics and Autonomous Systems, Volume 154, 2022, 104122, ISSN 0921-8890,		https://doi.org/10.1016/j.robot.2022.104122
Firefighting robot with deep learning and machine vision	Made a fire fighting robot which is capable of extinguishing fires caused by electric appliances using a deep learning and machine vision.	https://link.springer.com/article/10.1007/s00521-021-06537-y
An autonomous firefighting robot	They made an autonomous firefighting robot which used infrared and ultrasonic sensors to navigate and a flame sensor to detect fires.	https://ieeexplore.ieee.org/abstract/document/7251507?casa_token=MwygfhklafcAAAAA:EwxidirCpXeSbDYbQqz9b7b8V60N-BE1MAt0QVw4qqOw3jmN1ri3Dmxmlft5fPkoAU5GYCCv-g
Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning	A low resolution thermal camera is mounted on a remote controlled robot. The robot is trained to detect victims.	https://link.springer.com/chapter/10.1007/978-3-031-26889-2_32
Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics	The effectiveness of three different cameras for victim detection. Namely a; RGB, thermal and multispectral camera.	https://www.mdpi.com/2076-3417/14/2/766
Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot	Introduces an algorithm for a firefighting robot that finds fires using long wave infrared camera, ultraviolet radiation sensor and LIDAR.	https://ieeexplore.ieee.org/abstract/document/6584304?casa_token=LkAw2KTC4nYAAAAA:sfj76cZ9huUmUO-CDOGtj8YEuFbax9n_1bjf8qktH1_HyPR44yadjAo0pHykrJmxICOuE2jiEQ
On the Enhancement of Firefighting Robots using Path-Planning Algorithms		https://link.springer.com/article/10.1007/s42979-021-00578-9

Appendix

Appendix 1; Logbook

Logbook

Week	Name	Hours spent	Total hours
1	Dimitrios Adaos	Meeting (1h), Brainstorm (0.5h)
	Wiliam Dokov	Meeting (1h), Brainstorm (0.5h)
	Kwan Wa Lam	Meeting (1h), Brainstorm (0.5h), Find papers(1h)
	Kamiel Muller	Meeting (1h), Brainstorm (0.5h)
	Georgi Nihrizov	Meeting (1h), Brainstorm (0.5h)
	Twan Verhagen	Meeting (1h), Brainstorm (0.5h)
2	Dimitrios Adaos
	Wiliam Dokov
	Kwan Wa Lam
	Kamiel Muller
	Georgi Nihrizov
	Twan Verhagen
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