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Enhancing gas detection-based swarming through deep reinforcement learning

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Abstract

Swarm-Intelligence (SI), the collective behavior of decentralized and self-organized system, is used to efficiently carry out practical missions in various environments. To guarantee the performance of swarm, it is highly important that each object operates as an individual system while the devices are organized as simple as possible. This paper proposes an efficient, scalable, and practical swarming system using gas detection device. Each object of the proposed system has multiple sensors and detects gas in real time. To let the objects move toward gas rich spot, we propose two approaches for system design, vector-sum based, and Reinforcement Learning (RL) based. We firstly introduce our deterministic vector-sum-based approach and address the RL-based approach to extend the applicability and flexibility of the system. Through system performance evaluation, we validated that each object with a simple device configuration performs its mission perfectly in various environments.

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Acknowledgements

This research was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20204010600220) and the National Research Foundation of Korea funded by the Korean Government (grant 2020R1A2C1012389).

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  1. Sangmin Lee and Seongjoon Park contributed equally to this work.

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  1. Korea University, Seoul, Republic of Korea

    Sangmin Lee, Seongjoon Park & Hwangnam Kim

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  1. Sangmin Lee
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  2. Seongjoon Park
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  3. Hwangnam Kim
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Correspondence to Hwangnam Kim.

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Preliminary version of this paper appeared in the Proceedings of the 6th International Conference on Next Generation Computing 2020 (ICNGC).

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Lee, S., Park, S. & Kim, H. Enhancing gas detection-based swarming through deep reinforcement learning. J Supercomput 78, 14794–14812 (2022). https://doi.org/10.1007/s11227-022-04478-4

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