PRE2023 3 Group10
Group members
Name | Student number | 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 know 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
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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.
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
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Approach
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Planning
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Research papers
№ | Title | Summary | Link |
---|---|---|---|
1 | 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 |
2 | 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 |
3 | 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 |
4 | The role of robots in firefighting
Bogue, R. (2021), Industrial Robot, Vol. 48 No. 2, pp. 174-178. |
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. | https://www.emerald.com/insight/content/doi/10.1108/IR-10-2020-0222/full/html |
5 | SLAM for Firefighting Robots: A Review of Potential Solutions to Environmental Issues
Y. Hong, 2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)*, Ma'anshan, China, 2022, pp. 844-849, |
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. |
doi: 10.1109/WCMEIM56910.2022.10021457. |
6 | 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 |
7 | 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 |
8 | 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 |
9 | 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 |
10 | 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 |
11 | 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 |
12 | 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 |
13 | On the Enhancement of Firefighting Robots using Path-Planning Algorithms | Tests performance of several path-plannig algorithms to allow a firefighting robot to move more efficiently. | https://link.springer.com/article/10.1007/s42979-021-00578-9 |
14 | An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition | Made a firefighting robot that maps the area using an algorithm and uses a deep-learning-based flame detection technology utilizing a LIDAR. | https://www.mdpi.com/2571-6255/6/3/93 |
15 | Human Presence Detection using Ultra Wide Band Signal for Fire Extinguishing Robot | A remotely controlled robot using ultra-wide band radar detects humans while fire and smoke are present based on the persons respiration movement. | https://ieeexplore.ieee.org/document/9293893 |
16 | Firefighting Robot Stereo Infrared Vision and Radar Sensor Fusion for Imaging through Smoke | 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. | https://link.springer.com/article/10.1007/s10694-014-0413-6 |
17 | Global Path Planning for Fire-Fighting Robot Based on Advanced Bi-RRT 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. | https://ieeexplore.ieee.org/document/9516153 |
18 | Round-robin study of a priori modelling predictions of the Dalmarnock Fire Test One | Compares the results of different types of fire simulation models, with a real-world experiment. | https://www.sciencedirect.com/science/article/abs/pii/S0379711209000034 |
Appendix
Appendix 1; Logbook
Week | Name | Hours spent | Total hours |
---|---|---|---|
1 | Dimitrios Adaos | Meeting (1h), Brainstorm (0.5h), Find papers (2h),
Read and summarize papers (8h) |
|
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 |