Dahyeon Kim¹, Jiyoung Min¹, Yongwoo Song¹, Chulsu Kim², Junho Ahn^1,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2315-2331, 2023, DOI:10.32604/iasc.2023.034394

Abstract Existing firefighting robots are focused on simple storage or fire suppression outside buildings rather than detection or recognition. Utilizing a large number of robots using expensive equipment is challenging. This study aims to increase the efficiency of search and rescue operations and the safety of firefighters by detecting and identifying the disaster site by recognizing collapsed areas, obstacles, and rescuers on-site. A fusion algorithm combining a camera and three-dimension light detection and ranging (3D LiDAR) is proposed to detect and localize the interiors of disaster sites. The algorithm detects obstacles by analyzing floor segmentation and edge patterns using a mask… More >

Dahyeon Kim¹, Chulsu Kim², Junho Ahn^1,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2765-2781, 2022, DOI:10.32604/cmc.2022.025116

Abstract When firefighters are engaged in search and rescue missions inside a building at a risk of collapse, they have difficulty in field command and rescue because they can only simply monitor the situation inside the building utilizing old building drawings or robots. To propose an efficient solution for fast search and rescue work of firefighters, this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation, and identifying the information of obstacles which are risk factors, using an artificial intelligence algorithm based on low-cost robots. Our research separates the floor by using the mask… More >