Path Planning Dan Penghindaran Rintangan Multi-UAV Menggunakan Metode Ant Colony Optimization

Setyabudi, Ilham Nabiel (2025) Path Planning Dan Penghindaran Rintangan Multi-UAV Menggunakan Metode Ant Colony Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perencanaan jalur (path planning) dan penghindaran rintangan merupakan tantangan utama dalam pengoperasian kendaraan udara tak berawak Unmanned Aerial Vehicle (UAV), terutama ketika melibatkan banyak UAV (multi-UAV) dalam suatu area yang kompleks. Penelitian ini mengusulkan penerapan metode Ant Colony Optimization (ACO) untuk menyelesaikan masalah tersebut. ACO, sebagai algoritma optimasi berbasis perilaku semut, memiliki keunggulan dalam menemukan solusi optimal melalui pencarian jalur dengan mempertimbangkan jarak, waktu, dan tingkat keamanan masing-masing UAV. Penelitian ini melibatkan simulasi untuk menguji kemampuan ACO dalam menentukan jalur terbaik bagi multi-UAV dengan berbagai konfigurasi rintangan. Selain itu, algoritma ini juga dirancang untuk mencegah terjadinya tabrakan antar UAV dengan mempertimbangkan kontrol formasi seperti posisi awal, tujuan, dan pergerakan dinamis UAV. Hasil simulasi menunjukkan bahwa penggunaan ACO bersama dengan algoritma Path Stamping Forming (PSF) dan Artificial Potential Field (APF) mampu menghasilkan jalur yang efisien, menghindari rintangan secara efektif, dan mengurangi risiko interferensi antar UAV. Pendekatan ini diharapkan dapat diterapkan dalam berbagai aplikasi, seperti misi penyelamatan, pengawasan, pengiriman logistik, dan militer dengan meningkatkan efisiensi dan keberhasilan multi-UAV di lingkungan yang dinamis.
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Path planning and obstacle avoidance are major challenges in the operation of Unmanned Aerial Vehicles (UAVs), especially when involving multiple UAVs (multi-UAVs) in a complex area. This study proposes the application of the Ant Colony Optimization (ACO) method to solve these problems. ACO, as an ant-based optimization algorithm, has the advantage of finding optimal solutions through path finding by considering the distance, time, and safety level of each UAV. This study involves simulations to test the ability of ACO in determining the best path for multi-UAVs with various obstacle configurations. In addition, this algorithm is also designed to prevent collisions between UAVs by considering formation control such as initial position, destination, and dynamic movement of UAVs. Simulation results show that the use of ACO together with the Path Stamping Forming (PSF) and Artificial Potential Field (APF) algorithms is able to produce efficient paths, effectively avoid obstacles, and reduce the risk of interference between UAVs. This approach is expected to be applied in various applications, such as rescue missions, surveillance, logistics delivery, and military by improving the efficiency and success of multi-UAVs in dynamic environments.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Ant Colony Optimization, Artificial Potential Field (APF), Path Stamping Forming (PSF), Sistem Multi Agen, Multi Agent System, Unmanned Aerial Vehicle (UAV)
Subjects:	Q Science > Q Science (General) > Q337.3 Swarm intelligence Q Science > QA Mathematics > QA402 System analysis.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Ilham Nabiel Setyabudi
Date Deposited:	24 Jul 2025 03:26
Last Modified:	24 Jul 2025 03:26
URI:	http://repository.its.ac.id/id/eprint/120943

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