The unmanned swarm robot system, which enables multiple robots to collaborate and perform a variety of tasks, is extensively researched for its potential applications. Accurate determination of the location of swarm robots during operation is of paramount importance, and various positioning algorithms are employed to achieve this. Specifically, in situations where global positioning system (GPS) signals are unavailable, fixed anchor nodes with known location information can be utilized for localization. However, in scenarios where fixed anchor nodes are not present, and the robots operate in a swarm, applying this technology poses challenges, necessitating a localization technique that relies solely on distance information between the robots. This paper proposes a deep neural network (DNN) technique that utilizes only the distance information between moving nodes to predict the real-time relative coordinates of each node. It is assumed that the distances between nodes are updated sequentially and periodically according to a predetermined measurement cycle. A grid-based localization technique is used as the existing method for performance comparison. Computer simulation results demonstrate that the proposed DNN-based relative Localization technique exhibits superior localization performance compared to the existing Grid-based method. Furthermore, the proposed technique shows similar performance regardless of the distance measurement cycle, indicating that it is not significantly affected by the cycle. Therefore, applying the proposed relative Localization algorithm to swarm robots could enable real-time and accurate relative positioning, facilitating precise location tracking of the swarm.
Keywords
Relative Localization, Deep Neural Network, Distance, Swarm Robots, Moving Nodes.
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Eui-Rim Jeong
Eui-Rim Jeong
Artificial Intelligence, Hanbat National University, Daejeon, Republic of Korea.
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Cite this article
In-Young Hyun, Seung-Mi Yun and Eui-Rim Jeong, “DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots", pp. 504-511, April 2024. doi: 10.53759/7669/jmc202404048.
