Autonomous fire brigade robot

We proposed a fire detection algorithm based on image processing techniques that are compatible with surveillance devices like closed-circuit television CCTV, wireless cameras and autonomous fire detection robots. The algorithm utilizes the RGB color model to detect the color of the fire

Project Title

Autonomous fire brigade robot

Project Area of Specialization

Artificial Intelligence

Project Summary

We proposed a fire
detection algorithm based on image processing techniques that are compatible with surveillance
devices like closed-circuit television CCTV, wireless cameras and autonomous fire detection
robots. The algorithm utilizes the RGB color model to detect the color of the fire which is
mainly comprehended by the intensity of the component R which is red color. The growth of the
fire is detected using Sobel edge detection. Finally; a color-based segmentation technique was
applied based on the results from the RGB color model and the Sobel edge detection technique
to identify the region of interest (ROI) of the fire and to suppress the fire by water cannon.
After analyzing 40 different fire scenarios images, the final efficiency obtained from testing the
algorithm is about 88%. After getting such exactness and productivity it can be concluded from
this proposal that there is a requirement for such sort of autonomous fire detection robots or
systems. Disasters can be avoided with minimal risk to human life.

Project Objectives

We will design an autonomous fire brigade robot which allows us to safely extinguish the fire with
minimal human contact. By designing and implementing a remotely controlled robot capable
extinguishing flames, disasters can be avoided with minimal risk to human life.
To extinguish the fire a vehicle carrying the extinguishing canon will be designed. The vehicle
will move autonomously. The workers will be stationed far away from the explosion radius to
minimize the risk of human loss. Extinguisher canon will be operated with the help of water
pump to increase the water pressure coming out of the canon nozzle. The canon will also be
controlled autonomously.
The biggest advantage of this robot is the safety of human life from such disastrous incidents.
Minimum human contact is required in operating such robots from a long range. It is much
more efficient alternative than human. It can be used where there is a chance of radioactive or
chemical explosions and even human presence is dangerous.

Project Implementation Method

To extinguish the fire an autonomous vehicle carrying the
extinguishing canon will be designed.

The robot will detect fire through a camera using image
processing techniques.

The RGB color detection model will be used by the robot to detect
fire.

Extinguisher canon will be operated with the help of water pump
to increase the water pressure coming out of the canon nozzle.

To increase the precision and effectiveness of the robot, we are
using humidity and temperature sensors.

To stop the robot at a safe distance from the fire struck building
ultrasonic sensors are being used.

Humidity and temperature sensors will also help in keeping the
robot at a safe distance by using a threshold value of both factors.

Benefits of the Project

Economic:

The robot provides a safer but cheaper alternative for a
conventional fire brigade system.

Its maintenance is easy and cheap and technology can be upgraded
when necessary.

Social:

The robot’s main purpose is to provide safety for firefighters and
decrease the risks of loss of life.

Environmental:

The conventional fire trucks use fuels and emit smoke and
hazardous gases, the robot will not emit any such smoke as it is
battery driven.

Technical Details of Final Deliverable

Image Segmentation Techniques
The Image segmentation is referred to as one of the most important processes of image processing.
Image segmentation is the technique of dividing or partitioning an image into parts, called
segments. It is mostly useful for applications like image compression or object recognition,
because for these types of applications, it is inefficient to process the whole image. So, image
segmentation is used to segment the parts from image for further processing. There exist several
image segmentation techniques, which partition the image into several parts based on certain
image features like pixel intensity value, color, texture, etc. There are different segmentation
techniques which are as follows:
 Edge Based Segmentation Method
 Threshold Method
 Region Based Segmentation Method
 Clustering Based Segmentation Method
 Watershed Based Methods
 Partial Differential Equation Based Segmentation Method
 Artificial Neural Network Based Segmentation Method

Digital Image Processing
Digital image processing is the use of computer algorithms to perform image processing on
digital images. As a subcategory or field of digital signal processing, digital image processing
has many advantages over analog image processing. It allows a much wider range of algorithms
to be applied to the input data and can avoid problems such as the build-up of noise and signal
distortion during processing. Since images are defined over two dimensions (perhaps more) digital
image processing may be modeled in the form of multidimensional systems. The generation and
development of digital image processing are mainly affected by three factors:
 The development of computers.
 The development of mathematics (especially the creation and improvement of discrete
mathematics theory).
 The demand for a wide range of applications in environment, agriculture, military, industry
and medical science has increased.
DC motors are one of the motor types used for electric vehicles (EV) applications. Before
advances in power electronics, they were commonly used in variable speed applications. There
are two types of DC motors.
1. Brushed DC Motor
2. Brush-less DC Motor

Python (Programming language)
Python is an interpreted, high-level, general-purpose programming language. Created by Guido
van Rossum and first released in 1991, Python’s design philosophy emphasizes code readability
with its notable use of significant white space.

OpenCV (Open source computer vision)
OpenCV (Open source computer vision) is a library of programming functions mainly aimed
at real-time computer vision. Originally developed by Intel, it was later supported by Willow
Garage then Itseez (which was later acquired by Intel).

Raspberry Pi

Final Deliverable of the Project

Hardware System

Core Industry

Security

Other Industries

Transportation , Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Robotics

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com