Enhancing gas detection-based swarming through deep reinforcement learning

Sangmin Lee, Seongjoon Park, Hwangnam Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)14794-14812
Number of pages19
JournalJournal of Supercomputing
Volume78
Issue number13
DOIs
Publication statusPublished - 2022 Sep

Keywords

  • Multi-robot control
  • Reinforcement learning
  • Remote sensing
  • Swarm-intelligence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Information Systems
  • Hardware and Architecture

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