Implementasi Perencanaan Lintasan Mobile Robot Menggunakan Algoritma Improved A* dan Safe Artificial Potential Field (SAPF)

Ilham, Mohammad (2026) Implementasi Perencanaan Lintasan Mobile Robot Menggunakan Algoritma Improved A* dan Safe Artificial Potential Field (SAPF). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Mobile robot membutuhkan sistem navigasi yang mampu merencanakan lintasan secara efisien sekaligus menyesuaikan pergerakan terhadap kondisi lingkungan nyata, termasuk menghindari rintangan statis, rintangan dinamis, dan kondisi local minima. Penelitian ini mengimplementasikan sistem perencanaan lintasan mobile robot yang mengintegrasikan algoritma Improved A* sebagai global path planner untuk menghasilkan lintasan global yang efisien, dan Safe Artificial Potential Field (SAPF) sebagai local path planner untuk mengikuti lintasan tersebut serta memberikan respons penghindaran rintangan secara reaktif berbasis data LiDAR. Sistem diimplementasikan pada mobile robot Yahboom RDK X5 beroda mecanum menggunakan ROS 2 Humble, dengan lokalisasi Adaptive Monte Carlo Localization (AMCL) dan visualisasi melalui RViz2. Pengujian dilakukan pada empat skenario: tanpa rintangan, rintangan statis, lorong sempit (uji local minima), dan rintangan dinamis, dengan parameter evaluasi berupa keberhasilan mencapai tujuan, error lintasan, Mean Squared Error (MSE), waktu tempuh, dan jarak tempuh. Hasil menunjukkan robot berhasil mencapai tujuan pada seluruh skenario pengujian. Pada skenario tanpa rintangan diperoleh error rata-rata 0,0508 m dan MSE 0,0037 m², pada skenario rintangan statis error rata-rata 0,162 m, pada skenario lorong sempit 0,1108 m, dan pada skenario rintangan dinamis 0,095 m dengan robot memanfaatkan gerak holonomik roda mecanum untuk manuver penghindaran. Hasil ini membuktikan bahwa integrasi Improved A* dan SAPF mampu menghasilkan sistem navigasi mobile robot yang efisien, adaptif, dan andal dalam menangani variasi kondisi lintasan pada lingkungan nyata.
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Mobile robots require a navigation system capable of efficient path planning while adapting movement to real-world conditions, including avoidance of static obstacles, dynamic obstacles, and local minima. This research implements a mobile robot path planning system integrating the Improved A* algorithm as a global path planner to generate efficient global paths, and the Safe Artificial Potential Field (SAPF) as a local path planner to follow these paths while providing reactive obstacle avoidance based on LiDAR data. The system was implemented on a Yahboom RDK X5 mecanum-wheeled mobile robot using ROS 2 Humble, with Adaptive Monte Carlo Localization (AMCL) for localization and RViz2 for visualization. Testing was conducted across four scenarios: no obstacle, static obstacle, narrow corridor (local minima test), and dynamic obstacle, evaluated by goal-reaching success, path error, Mean Squared Error (MSE), travel time, and travel distance. Results show that the robot successfully reached the goal in all test scenarios. The average path errors obtained were 0.0508 m with MSE 0.0037 m² for the no-obstacle scenario, 0.1624 m for the static obstacle scenario, 0.1108 m for the narrow corridor scenario, and 0.095 m for the dynamic obstacle scenario, with the robot leveraging the holonomic motion of its mecanum wheels for avoidance maneuvers. These results demonstrate that the integration of Improved A* and SAPF produces an efficient, adaptive, and reliable mobile robot navigation system capable of handling varied path conditions in real-world environments.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Mobile Robot, Path Planning, Improved A*, Safe Artificial potential field, ROS2, AMCL, Mecanum Wheel.
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ211 Robotics. T Technology > TJ Mechanical engineering and machinery > TJ211.415 Mobile robots T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK6592.A9 Automatic tracking.
Divisions:	Faculty of Industrial Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Mohammad Ilham
Date Deposited:	27 Jul 2026 01:41
Last Modified:	27 Jul 2026 01:41
URI:	http://repository.its.ac.id/id/eprint/137455

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