Rozari, Yohakim (2025) Perencanaan Lintasan Unmanned Surface Vehicle Untuk Meminimumkan Waktu Tempuh Menggunakan Metode Improved Ant Colony Optimization Dan Safe Artificial Potential Field. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Navigasi otonom pada Unmanned Surface Vehicle (USV) memerlukan sistem perencanaan lintasan yang mampu menghasilkan jalur optimal sembari menghindari rintangan statis maupun dinamis. Penelitian ini mengembangkan algoritma perencanaan lintasan yang mengintegrasikan Improved Ant Colony Optimization (IACO) untuk perencanaan jalur global dengan Safe Artificial Potential Field (SAPF) untuk perencanaan jalur lokal guna meminimalkan waktu tempuh USV. Algoritma IACO dikembangkan dengan perbaikan fungsi heuristik, mekanisme pembaruan feromon lokal dan global berbasis Ant Colony System, dan penerapan constraint manuver untuk menghasilkan jalur yang dapat dilalui USV dengan lancar. Jalur global yang dihasilkan kemudian dihaluskan menggunakan metode G² Continuous Bezier Spiral untuk mengeliminasi sudut tajam. Untuk mengatasi rintangan dinamis, dikembangkan algoritma SAPF dengan mekanisme penghindaran adaptif yang memprediksi posisi masa depan USV dan rintangan. Simulasi dilakukan menggunakan MATLAB dengan model USV 6 DOF dan parameter rill. Hasil penelitian menunjukkan bahwa parameter IACO q_0=0.1 memiliki waktu komputasi tercepat yaitu 118.4 detik, kemudian proses path smoothing mampu mengurangi waktu tempuh hingga 29 detik dan jarak tempuh hingga 71.45 meter. Zona operasi vortex dominan pada SAPF menghasilkan performa optimal dengan waktu tempuh 420.90 detik, dan implementasi metode adaptif berhasil mengurangi waktu tempuh hingga 49 detik saat menghindari rintangan dinamis. Kombinasi metode yang dikembangkan terbukti mampu menghasilkan navigasi USV yang efisien dengan mempertahankan aspek keselamatan operasi
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Autonomous navigation in Unmanned Surface Vehicles (USV) requires a path planning system capable of generating optimal trajectories while avoiding both static and dynamic obstacles. This research develops a path planning algorithm that integrates Improved Ant Colony Optimization (IACO) for global path planning with Safe Artificial Potential Field (SAPF) for local path planning to minimize USV travel time. The IACO algorithm incorporates improvements in heuristic functions, local and global pheromone update mechanisms based on Ant Colony System, and implementation of maneuvering constraints to produce feasible paths for smooth USV navigation. The generated global path is then smoothed using G² Continuous Bezier Spiral method to eliminate sharp corners. To handle dynamic obstacles, a SAPF algorithm with adaptive avoidance mechanism is developed to predict future positions of both USV and obstacles. Simulations are conducted using MATLAB with realistic 6-DOF USV model and parameters. Results demonstrate that IACO parameters achieve the fastest computation time of 118.4 seconds, while path smoothing process reduces travel time by up to 29 seconds and travel distance by up to 71.45 meters. Vortex dominant zone in SAPF yield optimal performance with 420.90 seconds travel time, and adaptive method implementation successfully reduces travel time by up to 49 seconds when avoiding dynamic obstacles. The combination of developed methods proves capable of generating efficient USV navigation while maintaining operational safety aspects
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | USV, Ant Colony Optimization, Artificial Potential Field, perencanaan lintasan, navigasi. USV, Ant Colony Optimization, Artificial Potential Field, path planning, navigation |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control 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: | Yohakim Salomon Blanteran De Rozari |
| Date Deposited: | 25 Jul 2025 08:01 |
| Last Modified: | 25 Jul 2025 08:01 |
| URI: | http://repository.its.ac.id/id/eprint/121624 |
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