PRE2023 3 Group10: Difference between revisions

From Control Systems Technology Group
Jump to navigation Jump to search
No edit summary
No edit summary
Line 62: Line 62:
|-
|-
|Firefighting robot with deep learning and machine vision
|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
|An autonomous firefighting robot
|
|
|
|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
| Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning
|
|
|
|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
|Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
|
|
|
|https://www.mdpi.com/2076-3417/14/2/766
|-
|-
|Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot
|Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot
|
|
|
|https://ieeexplore.ieee.org/abstract/document/6584304?casa_token=LkAw2KTC4nYAAAAA:sfj76cZ9huUmUO-CDOGtj8YEuFbax9n_1bjf8qktH1_HyPR44yadjAo0pHykrJmxICOuE2jiEQ
|-
|-
|
|
Line 110: Line 110:
|}
|}


== Appendix ==
==Appendix==


=== Appendix 1; Logbook ===
===Appendix 1; Logbook ===
{| class="wikitable"
{| class="wikitable"
|+Logbook
|+Logbook
!Week
!Week
!Name
!Name
!Hours spent
!Hours spent  
!Total hours
!Total hours
|-
|-
Line 130: Line 130:
|-
|-
|Kwan Wa Lam
|Kwan Wa Lam
|Meeting (1h), Brainstorm (0.5h)
| Meeting (1h), Brainstorm (0.5h)
|
|
|-
|-
Line 138: Line 138:
|-
|-
|Georgi Nihrizov
|Georgi Nihrizov
|Meeting (1h), Brainstorm (0.5h)
| Meeting (1h), Brainstorm (0.5h)
|
|
|-
|-
Line 154: Line 154:
|
|
|-
|-
|Kwan Wa Lam
|Kwan Wa Lam  
|
|
|
|
Line 179: Line 179:
|
|
|-
|-
|Kwan Wa Lam
|Kwan Wa Lam  
|
|
|
|
Line 204: Line 204:
|
|
|-
|-
|Kwan Wa Lam
|Kwan Wa Lam  
|
|
|
|

Revision as of 10:48, 16 February 2024

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 https://www.sciencedirect.com/science/article/pii/S095741742302688X
The role of robots in firefighting https://www.emerald.com/insight/content/doi/10.1108/IR-10-2020-0222/full/html
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 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 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 https://www.mdpi.com/2076-3417/14/2/766
Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot https://ieeexplore.ieee.org/abstract/document/6584304?casa_token=LkAw2KTC4nYAAAAA:sfj76cZ9huUmUO-CDOGtj8YEuFbax9n_1bjf8qktH1_HyPR44yadjAo0pHykrJmxICOuE2jiEQ

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)
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