Sistem Path following Menggunakan MPC dan Obstacle Avoidance Berbasis Costmap Constraint pada Turtlebot4

Harahap, Fadlan Hafiz (2026) Sistem Path following Menggunakan MPC dan Obstacle Avoidance Berbasis Costmap Constraint pada Turtlebot4. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Sistem navigasi mobile robot memerlukan metode kontrol yang mampu melakukan path following secara akurat sekaligus menjaga keamanan navigasi terhadap obstacle pada lingkungan yang dinamis. Pada penelitian ini, controller Model Predictive Control (MPC) berbasis costmap constraint dikembangkan pada TurtleBot4 menggunakan ROS 2 Jazzy. MPC dirancang dengan memanfaatkan model kinematika non-holonomic robot, fungsi optimasi, serta constraint jarak aman yang diperoleh dari local costmap dan sensor LiDAR untuk menghasilkan perintah kontrol yang aman dan stabil. Selain itu, matriks pembobotan Q, R, dan RΔ ditentukan untuk mengevaluasi pengaruh parameter kontrol terhadap performa navigasi pada berbagai skenario pengujian. Kinerja MPC kemudian dibandingkan dengan controller Dynamic Window Based (DWB) pada beberapa skenario navigasi dengan obstacle dinamis di lingkungan simulasi Gazebo Harmonic dan implementasi robot nyata. Hasil pengujian menunjukkan bahwa MPC menghasilkan performa path following yang lebih baik dibandingkan DWB dengan peningkatan berdasarkan parameter mean Cross-Track Error sebesar 8.16%, 14.77%, dan 13.89% pada masing-masing skenario pengujian. MPC juga menghasilkan pergerakan robot yang lebih halus sehingga panjang lintasan aktual dan waktu navigasi menjadi sedikit lebih tinggi dibandingkan DWB. Meskipun demikian, trade-off tersebut masih dapat diterima karena peningkatan akurasi navigasi dan kemampuan penghindaran rintangan yang berhasil dicapai. Implementasi pada robot nyata menunjukkan bahwa MPC tetap mampu bekerja secara stabil meskipun terdapat pengaruh sensor noise, communication delay, dan ketidakpastian aktuator, dengan mean Cross-Track Error sebesar 0.0889 m, 0.0805 m, dan 0.1011 m pada tiga skenario pengujian. Hasil penelitian menunjukkan bahwa MPC berbasis costmap constraint efektif untuk meningkatkan performa navigasi dan layak diterapkan pada sistem navigasi mobile robot di lingkungan nyata.
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Mobile robot navigation systems require control methods capable of achieving accurate path following while maintaining safe navigation in dynamic environments. In this study, a Model Predictive Control (MPC) controller with costmap constraints was developed for a TurtleBot4 platform using ROS 2 Jazzy. The MPC controller was designed based on a non-holonomic robot kinematic model, an optimization function, and safety constraints obtained from a local costmap and LiDAR sensor data to generate stable and safe control commands. In addition, the weighting matrices Q, R, and RΔ were determined to evaluate the influence of controller parameters on navigation performance under different testing scenarios. The performance of MPC was then compared with the Dynamic Window Based (DWB) controller in several navigation scenarios involving dynamic obstacles in both the Gazebo Harmonic simulation environment and Real-world robot implementation. Experimental results showed that MPC achieved better path-following performance than DWB, with improvements in mean Cross-Track Error of 8.16%, 14.77%, and 13.89% across the three test scenarios. MPC also generated smoother robot motion, resulting in slightly longer path lengths and navigation times compared to DWB. Nevertheless, this trade-off was considered acceptable due to the improvements in navigation accuracy and obstacle avoidance capability. Real-world implementation further demonstrated that MPC maintained stable performance despite the presence of sensor noise, communication delays, and actuator uncertainties, achieving mean Cross-Track Errors of 0.0889 m, 0.0805 m, and 0.1011 m in the three experimental scenarios. These results indicate that the proposed costmap-constrained MPC is effective in improving navigation performance and is suitable for deployment in real-world mobile robot navigation systems.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Model Predictive Control, Path following, TurtleBot4, Costmap Constraint, Obstacle Avoidance, ROS2.
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:	Fadlan Hafiz Harahap
Date Deposited:	23 Jul 2026 09:08
Last Modified:	23 Jul 2026 09:08
URI:	http://repository.its.ac.id/id/eprint/136595

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