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All Journal Jurnal Minfo Polgan (JMP)
Johan Johan
Universitas IBBI, Indonesia
Computer Science & IT Library & Information Science Mathematics Social Sciences

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Utilizing Deep Reinforcement Learning, EGO-Swarm Parameter Control determines the path of the drone Conrad Bombongan; Tanda Selamat; Johan Johan; Waisen Waisen
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): FORTCOMING ISSUE : Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13489

Abstract

The recent attention garnered by the EGO-Swarm system is due to its successful generation of smooth drone flight paths in complex real-world environments. Among the various parameter values utilized by EGO-Swarm for path generation, the maximum speed and maximum acceleration of the drone play a critical role in enhancing flight performance. However, in the current EGO-Swarm setup, these parameter values remain fixed during path creation, despite the potential need for real-time adjustments in dynamic environments. Therefore, there is a need to dynamically adapt these values to the evolving real-time environment. This paper proposes a novel algorithm that dynamically adjusts the maximum speed and maximum acceleration using hierarchical deep reinforcement learning in response to real-time environmental changes. To assess the effectiveness of the proposed method, a comparative experiment is conducted between the existing EGO-Swarm algorithm and the proposed algorithm in a ROS simulation. The experimental results reveal that the proposed algorithm surpasses the existing EGO-Swarm algorithm in terms of average speed improvement and path length.(Zhou et al., 2021)
Co-Authors Conrad Bombongan Tanda Selamat Waisen Waisen