Internet Connected Modern Fire Fighting Robot

Sabhanayagam, T; Kumar, T. Senthil; Narendra, M Shekokar; Sahayaraj, J. Martin

doi:10.1088/1742-6596/1964/4/042088

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Robots using various technologies have been investigated in various fields, including robots that manage power transmission towers [38], harvest strawberries in smart farms [39], and spray water by detecting heat and gas to perform firefighting [40]. The robots developed in such studies can improve our daily lives.…”

Section: Robot and Sensormentioning

confidence: 99%

Intelligent SLAM Algorithm Fusing Low-Cost Sensors at Risk of Building Collapses

Kim¹,

Ahn²

2023

Computers, Materials &Amp; Continua

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When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire, they use old architectural drawings or a simple monitoring method involving a video device attached to a robot. However, using these methods, the disaster situation inside a building at risk of collapse is difficult to detect and identify. Therefore, we investigate the generation of digital maps for a disaster site to accurately analyze internal situations. In this study, a robot combined with a low-cost camera and twodimensional light detection and ranging (2D-lidar) traverses across a floor to estimate the location of obstacles while drawing an internal map of the building. We propose an algorithm that detects the floor and then determines the possibility of entry, tracks collapses, and detects obstacles by analyzing patterns on the floor. The robot's location is estimated, and a digital map is created based on Hector simultaneous localization and mapping (SLAM). Subsequently, the positions of obstacles are estimated based on the range values detected by 2D-lidar, and the position of the obstacles are identified on the map using the map update method in semantic SLAM. All equipment are implemented using low-specification devices, and the experiments are conducted using a low-cost robot that affords near-real-time performance. The experiments are conducted in various actual internal environments of buildings. In terms of obstacle detection performance, almost all obstacles are detected, and their positions identified on the map with a high accuracy of 89%.

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Section: Robot and Sensormentioning

confidence: 99%