Modelling, Simulation and Mechatronics Design of a Wireless Automatic Fire Fighting Surveillance Robot

Madhevan, B.; R, Sakkaravarthi; Singh, Geeta; Diya, R.; Jha, Durgesh K.

doi:10.14429/dsj.67.10237

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…V-rep is used to create a robot simulation in a fire environment of fire simulation. His method is only based on the model, there is no actual experimental operation, and the persuasion is not strong [3]. e method proposed in this paper is a great progress of robot intelligent control technology and computer technology.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Control System of Fire Rescue Robot for High‐Rise Building Design

Yu¹

2022

Advances in Civil Engineering

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In recent years, with the steady and rapid growth of the national economy, various industries have developed rapidly and the resulting fire accidents have also been on the rise. This study mainly discusses the control system of a fire rescue robot for a high-rise building design. Robot fire detection method involves obtaining the video image of the fire detection area through a high-definition camera, performing image preprocessing on the current frame image of the video in the robot operating system to obtain the image; and performing the flame area segmentation based on the Ohta color space and the Otsu threshold segmentation algorithm on the image, to obtain the segmented image; through the interframe difference method, the preprocessed image of the previous frame is subtracted from the current frame image and the moving area of the image is segmented to obtain the segmented image; the obtained segmented image is combined with the segmented image in the robot operating system and images are intersected to obtain a segmented image with the characteristics of flame motion; based on other characteristics of the flame, flame recognition is performed on the area in the segmented image. Fires in high-rise buildings are increasing gradually, which seriously endangers the safety of human life and property. The firefighting robot and its control system play an important role in the fire protection of high-rise buildings. The purpose of this study is to analyze the disaster relief effect of a firefighting robot and control system in high-rise buildings. In this study, we use MATLAB software to model and simulate the rescue situation of a firefighting robot in high-rise buildings. The fuzzy control system established in the fuzzy logic toolbox of MATLAB can easily replace human field work and can change the control rules and membership function in the FIS editor. The results show that the rise time and adjustment time of the system are basically the same under the condition of variable load stiffness. The maximum overshoot is 0.59%, and the steady-state error is 0.19%. The maximum overshoot is 1.0148%, and the stability error is 0.46%. It is concluded that the expert PID algorithm is efficient and practical. It can be concluded that the robot position control system adopts PID control algorithm, and the attitude control system adopts expert PID control algorithm. This research provides some value for the development and design of firefighting robots in high-rise buildings in the future and also brings important significance.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Control System of Fire Rescue Robot for High‐Rise Building Design

Yu¹

2022

Advances in Civil Engineering

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“…The consideration of Gator tech smart house to be a programmable pervasive space [7]. The detailed discussion on the current state of smart homes and the discussion on the WSN-based smart sensors and actuators for intelligent building power management [8][9]. There are some investigations were conducted on the intelligent self-adjusting sensor based on ZigBee communications for smart home services where the structure composition, design concept, and implementation approach has described [ 10].…”

Section: Development Of Iot-based Fire Hazard Solutionsmentioning

confidence: 99%

Development of IoT-Based Automated Dynamic Emergency Response System Against Fire Incidents in Academic Building

Zakaria Al-Hady,

Islam,

Rashid

2023

IJEMM

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The ongoing advancement of architectural and structural designs, high-ceiling spaces, special spaces have made fire disasters increasingly diverse and difficult to predict. It is demanding the need for improved firefighting systems. This study aims to address the need for improved firefighting systems in academic building by proposing the development of an IoT-based automated emergency response website. The proposed system leverages IoT technology, wireless and bluetooth sensor networks to gather real-time data from various sensors and devices installed in the site and uses machine learning algorithms to predict and prevent potential fire incidents. The system also includes an emergency response website that allows users to access real-time information about the fire incident, location, severity, and evacuation instructions. Additionally, the proposed system incorporates Building Information Modelling (BIM) to optimize evacuation and rescue routes, providing early detection and accurate alarm capabilities, evacuation guidance for endangered individuals, and guidance for firefighters. The integration of BIM allows the system to provide a three-dimensional visualization of the site, enabling a more efficient and effective response to fire incidents. Overall, the proposed system aims to improve the safety and security through real-time monitoring and response capabilities. By leveraging the power of IoT technology, machine learning algorithms and BIM, the proposed system aims to reduce the impact of fire disasters by providing accurate and timely information, route optimization and facilitating effective evacuation and rescue efforts.Keywords: Building Information Modeling, IoT, Sensor, Dijkstra Algorithm, Simulation

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“…A DL model must be efficient enough to work in real-time to be suitable for running in a hardware-restricted system or mobile devices. For example, the development of the fire recognition DL model can work in the mobile vehicle system for fire detection proposed by Madhevan et al [19].…”

Section: Related Workmentioning

confidence: 99%

KutralNet: A Portable Deep Learning Model for Fire Recognition

Ayala

Fernandes

Cruz

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Most of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to perform the detection. The image approach is promising since it does not need specific sensors and can be easily embedded in different devices. However, besides the high performance, the computational cost of the used deep learning methods is a challenge to their deployment in portable devices. In this work, we propose a new deep learning architecture that requires fewer floating-point operations (flops) for fire recognition. Additionally, we propose a portable approach for fire recognition and the use of modern techniques such as inverted residual block, convolutions like depth-wise, and octave, to reduce the model's computational cost. The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops. One of our models presents 71% fewer parameters than FireNet, while still presenting competitive accuracy and AUROC performance. The proposed methods are evaluated on FireNet and FiSmo datasets. The obtained results are promising for the implementation of the model in a mobile device, considering the reduced number of flops and parameters acquired.

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Modelling, Simulation and Mechatronics Design of a Wireless Automatic Fire Fighting Surveillance Robot

Cited by 14 publications

References 9 publications

[Retracted] Control System of Fire Rescue Robot for High‐Rise Building Design

[Retracted] Control System of Fire Rescue Robot for High‐Rise Building Design

Development of IoT-Based Automated Dynamic Emergency Response System Against Fire Incidents in Academic Building

KutralNet: A Portable Deep Learning Model for Fire Recognition

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