Pengembangan Kontroler Fuzzy-Dwa untuk Path Following dan Obstacle Avoidance pada Nonholonomic Mobile Robot

Naifah, Putri Aqila (2026) Pengembangan Kontroler Fuzzy-Dwa untuk Path Following dan Obstacle Avoidance pada Nonholonomic Mobile Robot. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Navigasi pada mobile robot nonholonomic memerlukan kemampuan path following dan obstacle avoidance yang adaptif terhadap perubahan kondisi lingkungan. Dynamic Window Approach (DWA) merupakan salah satu metode yang banyak digunakan sebagai kontroler lokal, namun penggunaan bobot evaluasi yang tetap menyebabkan kemampuan adaptasinya menjadi terbatas. Penelitian FDWA sebelumnya telah memanfaatkan logika fuzzy untuk menyesuaikan bobot DWA, tetapi belum mempertimbangkan kecepatan aktual robot sebagai bagian dari proses pengambilan keputusan serta masih menggunakan jumlah aturan fuzzy yang relatif besar. Penelitian ini mengusulkan metode Fuzzy Dynamic Window Approach (FDWA) dengan menambahkan kecepatan linear sebagai input fuzzy. Sistem fuzzy Sugeno menghasilkan bobot evaluasi DWA berupa α, β, dan γ secara langsung menggunakan satu Fuzzy Inference System dengan 18 aturan. Metode diimplementasikan pada ROS 2 Jazzy dengan menggunakan model robot TurtleBot4 dan dievaluasi melalui tiga skenario simulasi serta implementasi robot nyata. Hasil pengujian menunjukkan bahwa metode yang diusulkan mampu meningkatkan performa navigasi dibandingkan FDWA sebelumnya. Pada tiga skenario simulasi, navigation time berkurang sebesar 24,5–54,5%, mean linear velocity meningkat sebesar 27,7–94,3%, dan RMSE Cross-Track Error menurun sebesar 65,7–78,0%. Selain itu, penyederhanaan struktur fuzzy berhasil menurunkan waktu komputasi kontroler sebesar 65,0–75,9%. Hasil implementasi juga menunjukkan bahwa kontroler tetap mampu mencapai tujuan dan menghindari rintangan meskipun terdapat pengaruh noise sensor dan latency komunikasi.
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Navigation of nonholonomic mobile robots requires adaptive path following and Obstacle-avoidance capabilities to cope with dynamic environmental conditions. The Dynamic Window Approach (DWA) is widely used as a controller; however, its performance is often limited by fixed evaluation weights that reduce adaptability during navigation. Previous Fuzzy Dynamic Window Approach (FDWA) studies have introduced fuzzy logic to adjust DWA weights adaptively, but they generally do not consider the robot's current velocity as part of the decision-making process and often employ relatively complex fuzzy rule structures. This study proposes an FDWA method that incorporates the robot's current velocity together with obstacle distance and heading error as inputs to a Sugeno Fuzzy Inference System. The fuzzy system directly generates the DWA evaluation weights α, β, and γ using a single FIS consisting of 18 rules. The proposed controller was implemented in ROS 2 Jazzy on a TurtleBot4 platform and evaluated through three simulation scenarios and real-world experiments. The results demonstrate that the proposed method improves navigation performance compared with a previous FDWA approach. Across the three simulation scenarios, navigation time was reduced by 24.5–54.5%, mean linear velocity increased by 27.7–94.3%, and Cross-Track Error RMSE decreased by 65.7–78.0%. Furthermore, the simplified fuzzy structure reduced controller computation time by 65.0–75.9%. Experimental results on the real robot also showed that the controller was able to successfully reach the goal while avoiding obstacles despite the presence of sensor noise and communication latency.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Fuzzy Dynamic Window Approach, mobile robot, path following, obstacle avoidance, ROS 2, TurtleBot4.
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ211 Robotics. T Technology > TJ Mechanical engineering and machinery > TJ211.415 Mobile robots T Technology > TJ Mechanical engineering and machinery > TJ217 Adaptive control systems T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Putri Aqila Naifah
Date Deposited:	23 Jul 2026 07:45
Last Modified:	23 Jul 2026 07:45
URI:	http://repository.its.ac.id/id/eprint/136597

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