| Title | Artificial Intelligence and Internet of Things for Robotic Disaster Response |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lee, M.-F. R., Chien, T.-W. |
| Conference Name | 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) |
| Date Published | aug |
| Keywords | AIoT, artificial intelligence, Big Data, composability, compositionality, control engineering computing, deep learning model training, Disaster Response, disaster site, disaster-straining robots, disasters, emergency management, environmental Big Data, field workstation, Fukushima nuclear disaster, government agencies, ground robots, image classification, intelligent Internet of Things, Internet of Things, learning (artificial intelligence), Mobile Robot, mobile robots, model verification, natural man-made disasters, neural nets, on-site continuing objects classification, pubcrawl, Robot sensing systems, robotic disaster response, Service robots, surface robots, swarm intelligence, underwater swarm robots, Unmanned underwater vehicles, Wenchuan earthquake, Workstations |
| Abstract | After the Fukushima nuclear disaster and the Wenchuan earthquake, the relevant government agencies recognized the urgency of disaster-straining robots. There are many natural or man-made disasters in Taiwan, and it is usually impossible to dispatch relevant personnel to search or explore immediately. The project proposes to use the architecture of Intelligent Internet of Things (AIoT) (Artificial Intelligence + Internet of Things) to coordinate with ground, surface and aerial and underwater robots, and apply them to disaster response, ground, surface and aerial and underwater swarm robots to collect environmental big data from the disaster site, and then through the Internet of Things. From the field workstation to the cloud for "training" deep learning model and "model verification", the trained deep learning model is transmitted to the field workstation via the Internet of Things, and then transmitted to the ground, surface and aerial and underwater swarm robots for on-site continuing objects classification. Continuously verify the "identification" with the environment and make the best decisions for the response. The related tasks include monitoring, search and rescue of the target. |
| DOI | 10.1109/ARIS50834.2020.9205794 |
| Citation Key | lee_artificial_2020 |
Artificial Intelligence and Internet of Things for Robotic Disaster Response