A Study on the Design and Implementation of Multi-Disaster Drone System Using Deep Learning-Based Object Recognition and Optimal Path Planning


KIPS Transactions on Computer and Communication Systems, Vol. 10, No. 4, pp. 117-122, Apr. 2021
https://doi.org/10.3745/KTCCS.2021.10.4.117,   PDF Download:
Keywords: ACO, Swarm, Drone, Deep Learning, Path Planning
Abstract

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.


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Cite this article
[IEEE Style]
J. Kim, T. Lee, Y. Han and H. Byun, "A Study on the Design and Implementation of Multi-Disaster Drone System Using Deep Learning-Based Object Recognition and Optimal Path Planning," KIPS Transactions on Computer and Communication Systems, vol. 10, no. 4, pp. 117-122, 2021. DOI: https://doi.org/10.3745/KTCCS.2021.10.4.117.

[ACM Style]
Jin-Hyeok Kim, Tae-Hui Lee, Yamin Han, and Heejung Byun. 2021. A Study on the Design and Implementation of Multi-Disaster Drone System Using Deep Learning-Based Object Recognition and Optimal Path Planning. KIPS Transactions on Computer and Communication Systems, 10, 4, (2021), 117-122. DOI: https://doi.org/10.3745/KTCCS.2021.10.4.117.