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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Otomasi Kontrol dan Instrumentasi
Agustinus Tamba, Tua
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Multi quadrotors coverage optimization using reinforcement learning with negotiation Bonaventura Wijaya, Glenn; Agustinus Tamba, Tua
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2978-2986

Abstract

This paper proposes an optimization scheme to maximize the area coverage of multiple quadrotor unmanned aerial vehicles that are deployed to monitor an operational area/space. Each quadrotor initially performs a single agent reinforcement learning to determine target points with optimal coverage area. Whenever each quadrotor encounters the others within a predetermined negotiation region that is defined by an inter-agent distance threshold, it will activate a multiagent reinforcement learning with action negotiation algorithm and coordinate its movement policies to maximize the total coverage area and avoids inter-agent coverage overlaps. Results of simulation evaluations are shown to illustrate the performance of the proposed learning-based coverage optimization method.
Co-Authors Bonaventura Wijaya, Glenn Gilang Buana Putra, Leonardus Nathanlius, David Rizky Octavia, Ajeng Wahab, Faisal