A Flame Sensor-Based Firefighting Assistance Robot with Simulation Based Multi-Robot Implementation

Bankapur, Karthik; Mathur, Hritik; Singh, Himmat; Gupta, Akshit

doi:10.1109/accai53970.2022.9752470

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…The level of scientific research, scientific and technological strength, and the distribution of scientific research achievements can be revealed by the analysis of research institutions [42]. With the help of Gephi, a co-occurrence analysis of coauthor relations of the 20 authors (listed in Table 5) is generated in the form of a social network with 39 nodes and 22 links, as shown in Figure 5.…”

Section: Analysis Of Article Publicationsmentioning

confidence: 99%

A bibliometric analysis of worldwide cancer research using machine learning methods

et al. 2023

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Section: Analysis Of Article Publicationsmentioning

confidence: 99%

A bibliometric analysis of worldwide cancer research using machine learning methods

et al. 2023

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“…However, these studies are insufficient for firefighting support due to the limitations and characteristics given in Table 1 and Figure 3 [30]. Additionally, although there are several studies on sensor fusion for firefighting robots [31][32][33][34][35], these studies are related not to object detection in dense smoke environments but to flame detection or navigation in a general fire environment. In other words, studies on sensor fusion for object detection in dense smoke environments are very rare.…”

Section: Introductionmentioning

confidence: 99%

Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments

Hahn

2022

Applied Sciences

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In this paper, we propose a conceptual framework for a sensor fusion system that can detect objects in a dense smoke environment with a visibility of less than 1 m. Based on the review of several articles, we determined that by using a single thermal IR camera, a single Frequency-Modulated Continuous-Wave (FMCW) radar, and multiple ultrasonic sensors simultaneously, the system can overcome the challenges of detecting objects in dense smoke. The four detailed methods proposed are as follows: First, a 3D ultrasonic sensor system that detects the 3D position of an object at a short distance and is not affected by temperature change/gradient; Second, detecting and classifying objects such as walls, stairs, and other obstacles using a thermal IR camera; Third, a 2D radial distance measurement method for a distant object using an FMCW radar; Fourth, sensor fusion for 3D position visualization of multiple objects using a thermal IR camera, 3D ultrasonic sensor system, and FMCW radar. Finally, a conceptual design is presented based on the proposed methodologies, and their theoretical usefulness is discussed. The framework is intended to motivate future research on the development of a sensor fusion system for object detection in dense smoke environments.

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A Flame Sensor-Based Firefighting Assistance Robot with Simulation Based Multi-Robot Implementation

Cited by 2 publications

References 20 publications

A bibliometric analysis of worldwide cancer research using machine learning methods

A bibliometric analysis of worldwide cancer research using machine learning methods

Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments

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