Perencanaan Lintasan USV Untuk Menghindari Rintangan Menggunakan Algoritma Particle Swarm Optimization

Nugraha, Lambang Djati (2024) Perencanaan Lintasan USV Untuk Menghindari Rintangan Menggunakan Algoritma Particle Swarm Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Latar belakang optimasi path planning pada Unmanned Surface Vehicles (USV) sangat relevan dalam pengembangan teknologi kelautan dan otonomi. Path planning adalah pendekatan untuk merencanakan rute optimal guna mencapai tujuan tertentu, dengan memperhatikan kendala lingkungan dan sumber daya yang ada. Path planning yang baik dapat mengoptimalkan lintasan dan mengurangi risiko kegagalan atau kecelakaan. Pemilihan jalur yang tepat juga berkontribusi pada stabilitas dan kinerja USV. Salah satu tantangan dalam path planning adalah mengoptimalkan rute USV di perairan yang kompleks dengan memperhitungkan kondisi lingkungan, rintangan, dan arus laut menggunakan metode Particle Swarm Optimization (PSO). Metode ini bertujuan untuk merencanakan jalur yang mengurangi perlawanan air dan menghindari rintangan, sehingga meningkatkan jangkauan dan ketahanan operasional USV. PSO adalah algoritma optimasi heuristik yang terinspirasi oleh perilaku kelompok partikel dalam pencarian makanan pada burung. Dalam konteks USV, path planning menggunakan PSO menghasilkan jalur optimal atau solusi terbaik yang ditemukan oleh algoritma PSO di lingkungan perairan tertentu. Jalur ini mencakup koordinat titik-titik yang membentuk rute optimal bagi USV untuk mencapai tujuannya, dengan mempertimbangkan faktor seperti dinamika perairan, arus laut, rintangan, dan kriteria lain yang didefinisikan dalam fungsi objektif. Path planning menggunakan algoritma PSO USV dapat menentukan rute yang baik untuk menghindari rintangan dinamis. Metode PSO memungkinkan USV untuk cepat beradaptasi dengan perubahan kondisi lingkungan, seperti perubahan arus laut, cuaca, atau adanya rintangan baru di jalur.
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The background of path planning optimization in Unmanned Surface Vehicles (USV) is highly relevant in the development of marine technology and autonomy. Path planning is an approach used to plan an optimal route to achieve specific objectives, considering environmental constraints and available resources. Effective path planning can optimize the trajectory and reduce the risk of failure or accidents. Selecting the right path also contributes to the stability and performance of the USV. One of the challenges in path planning is optimizing the USV's route in complex waters by accounting for environmental conditions, obstacles, and ocean currents using the Particle Swarm Optimization (PSO) method. This method aims to plan routes that reduce water resistance and avoid obstacles, thereby increasing the operational range and endurance of the USV. PSO is a heuristic optimization algorithm inspired by the swarm behavior of particles searching for food in bird flocks. In the context of USVs, path planning using PSO results in an optimal path or the best solution found by the PSO algorithm in a specific aquatic environment. This path includes coordinates that form the optimal route for the USV to reach its goal, considering factors such as water dynamics, ocean currents, obstacles, and other criteria defined in the objective function. Path planning using the PSO algorithm is expected to minimize the USV's energy consumption and determine a good route to avoid dynamic obstacles. The PSO method allows the USV to quickly adapt to changing environmental conditions, such as ocean currents, weather changes, or the presence of new obstacles in the path.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Particle Swarm Optimization,Unmanned Surface Vehicle,Path Planning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK6592.A9 Automatic tracking. T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Nugraha Lambang Djati
Date Deposited:	31 Jul 2024 02:58
Last Modified:	31 Jul 2024 02:58
URI:	http://repository.its.ac.id/id/eprint/110896

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