Collaborative Robotic Framework for Emergency Situations Management in Areas of Flood and Landslide Disasters
Issued Date
2026-01-01
Resource Type
ISSN
23673370
eISSN
23673389
Scopus ID
2-s2.0-105032514792
Journal Title
Lecture Notes in Networks and Systems
Volume
1633 LNNS
Start Page
251
End Page
262
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Networks and Systems Vol.1633 LNNS (2026) , 251-262
Suggested Citation
Magid E., Tsoy T., Matsuno F., Suthakorn J., Svinin M. Collaborative Robotic Framework for Emergency Situations Management in Areas of Flood and Landslide Disasters. Lecture Notes in Networks and Systems Vol.1633 LNNS (2026) , 251-262. 262. doi:10.1007/978-3-032-05754-9_23 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115813
Title
Collaborative Robotic Framework for Emergency Situations Management in Areas of Flood and Landslide Disasters
Author(s)
Corresponding Author(s)
Other Contributor(s)
Abstract
The Southeast Asia region is vulnerable to extreme precipitation, leading to hydrological disasters that endanger lives and infrastructure. Rapid response measures necessitate search and rescue operations, where rescue robotics can replace human rescuers and provide supplementary capabilities. Effective rescue efforts require information on victim whereabouts and area mapping, necessitating the development of AI-based information systems. This paper outlines an international framework for using heterogeneous robotic teams and developing information collection systems for hazardous site rescue management. The approach leverages expertise in urban search and rescue robotics from Japan, Thailand, and Russia, countries frequently affected by high precipitation and climate change. The joint research aims to create a new framework and control strategies for cooperative behavior among international robotic teams, focusing on interaction protocols, mapping agreements, data fusion, and other collaborative features. The robotic teams comprise various unmanned ground vehicles, aerial vehicles, underwater vehicles, and surface vehicles. These teams provide local data through sensing and mapping activities from water surfaces, underwater, air, and terrain to create a comprehensive disaster site map. The collaborative framework relies on path planning, disaster area coverage algorithms, control strategies, and multi-robot joint SLAM technologies for heterogeneous teams. Robot Operating System (ROS) and Gazebo simulator are used for modeling and validating the algorithms.