Kontrol Formasi Dan Penghindaran Rintangan Pada Sistem Multi-Uav Menggunakan Metode Complex Laplacian Dan Modified Artificial Potential Field

Alfino, Alexandro (2025) Kontrol Formasi Dan Penghindaran Rintangan Pada Sistem Multi-Uav Menggunakan Metode Complex Laplacian Dan Modified Artificial Potential Field. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Unmanned Aerial Vehicle (UAV) adalah kendaraan otonom dengan derajat kebebasan tinggi dan biaya operasi rendah, serta mampu mengatasi keterbatasan sistem terestrial dalam hal aksesibilitas dan keandalan. Perkembangan teknologi UAV telah sampai pada penerapan kontrol formasi multiagen di mana sejumlah UAV dapat berkoordinasi maupun berkomunikasi untuk melakukan tujuan bersama di lingkungan kerja yang kompleks. Penelitian ini akan mengembangkan dan menguji integrasi algoritma kontrol formasi berbasis Complex Laplacian dengan algoritma penghindaran rintangan berbasis Modified Artificial Potential Field (MAPF) pada sistem multi-UAV. Algoritma MAPF digunakan untuk menghindari rintangan statis maupun dinamis secara adaptif berdasarkan gerak agen lainnya , dan algoritma Complex Laplacian digunakan sebagai pengatur formasi agar tetap stabil selama pergerakan. Penggabungan algoritma complex laplacian dan MAPF memiliki keunggulan sebesar 40.34% dalam meminamlisir error formasi dibandingkan metode MAPF pada pengujian dengan lingkungan tanpa halangan. Pada pengujian kontrol formasi dengan penghindaran rintangan pengujian integrasi unggul pada fase stabilitas dengan penurunan sebesar 14.766% dibanding dengan hanya MAPF untuk keseluruhan sistem. Error tersebut masih dapat ditolerir karena pada dasarnya penelitian ini ingin mengetahui tingkat keberhasilan suatu sistem multi–UAV dengan memadukan algoritma complex laplacian dan juga MAPF dalam melakukan tugas penghindaran rintangan serta mencegah tabrakan antar agen saat bergerak menuju titik tujuan.
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Unmanned Aerial Vehicle (UAV) is an autonomous vehicle with high degrees of freedom and low operating costs, and is able to overcome the limitations of terrestrial systems in terms of accessibility and reliability. The development of UAV technology has reached the implementation of multiagent formation control where a number of UAVs can coordinate and communicate to achieve common goals in complex work environments. This research will develop and test the integration of a Complex Laplacian-based formation control algorithm with a Modified Artificial Potential Field (MAPF)-based obstacle avoidance algorithm in a multi-UAV system. The MAPF algorithm is used to avoid static and dynamic obstacles adaptively based on the motion of other agents, and the Complex Laplacian algorithm is used as a formation regulator to remain stable during movement. The combination of the complex laplacian and MAPF algorithms can minimize formation errors by 40.34% in tests with an unobstructed environment. During the formation control testing with obstacle avoidance, the integrated approach demonstrated superior performance in the stability phase, with a 14.766% reduction compared to the system using only MAPF. This error can still be tolerated because basically this research wants to know the success level of a multi-UAV system by combining the complex laplacian algorithm and also MAPF in carrying out obstacle avoidance tasks and preventing collisions between agents when moving towards the destination point.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Kontrol Formasi, Penghindaran Rintangan, Modifikasi Artificial Potential Field (APF),Multiagen,Quadcopter. ======================================================= Control Formation, Obtacle avoidance, Modified Artificial Potential Field (APF),Multiagent, Quadcopter
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles. U Military Science > UG1242 Drone aircraft--Control systems. (unmanned vehicle)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Alexandro Alfino
Date Deposited:	24 Jul 2025 02:45
Last Modified:	24 Jul 2025 02:45
URI:	http://repository.its.ac.id/id/eprint/120976

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