PRE2023 3 Group10

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

Introduction

Problem statement

Robot for saving victims in a fire

Objectives

...

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.

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.

Requirements

...

Approach

...

Planning

...

Research papers

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	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.	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.	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.	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,	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.	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
An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition		https://www.mdpi.com/2571-6255/6/3/93
Human Presence Detection using Ultra Wide Band Signal for Fire Extinguishing Robot		https://ieeexplore.ieee.org/document/9293893
Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Smoke		https://link.springer.com/article/10.1007/s10694-014-0413-6
Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT Algorithm		https://ieeexplore.ieee.org/document/9516153

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