Sudianto, Achmad Imam and Muhammad Aziz Muslim, S.T., M.T., Ph.D and Dr. Ir. Dipl. Ing. Mochammad Rusli., - (2023) Model Predictive Control (MPC) Pada Navigasi Differential Wheeled Robot Dengan Menggunakan Dual Sensor,. Magister thesis, Universitas Brawijaya.
Abstract
Setiap misi robot mobile dimulai dengan pergerakan robot ke lokasi tugas. Dari sana, robot menjalankan tugasnya. Sistem kontrol diperlukan untuk memindahkan aktuator robot mobile (yang mungkin berbentuk roda atau kaki) dan memahami lingkungan sekitar robot untuk melakukan gerakan ini (persepsi). Penelitian ini bertujuan untuk mengembangkan teknik untuk mengontrol gerakan robot sambil mendeteksi rintangan dan jarak ke arah suatu objek. Robot dilengkapi dengan LIDAR dan kamera untuk melakukan tugas-tugas ini. Kontrol dibagi menjadi dua bagian utama, yaitu low-level controller dan high-level controller. Sebagai bagian dari low-level controller, metode Model Predictive Control (MPC) diusulkan untuk membantu kontrol roda sementara pendekatan Artificial Neural Network (ANN) digunakan dalam penelitian ini untuk mengidentifikasi rintangan dan metode Convolutional Neural Network (CNN) untuk mendeteksi objek, baik ANN dan CNN sebagai kontrol untuk bagian pengontrol tingkat tinggi robot.
English Abstract
Every mobile robot mission starts with the robot being moved to the task site. From there, the robot executes its tasks. A control system is required to move the mobile robot's actuator (which may be in the shape of wheels or legs) and comprehend the environment around the robot to perform these movements (perception). This research aims to develop a technique to control a robot's movement while detecting obstacles and distances toward an object. The robot is equipped with LIDAR and a camera to perform these tasks. The control is divided into two major parts, low-level and high-level controller. As part of a low-level controller robot, the Model Predictive Control (MPC) method is proposed to help with the control of wheel while the Artificial Neural Network (ANN) approach is used in this study to identify obstacles and the Convolutional Neural Network (CNN) method for detecting objects, both ANN and CNN as control for the high-level part of the robot.
Other obstract
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| Item Type: | Thesis (Magister) |
|---|---|
| Identification Number: | '0423070008 |
| Uncontrolled Keywords: | LIDAR, Kamera, Artificial Neural Network, Model Predictive Control, Convolutional Neural Network, Mobile Robot,-LIDAR, Camera, Artificial Neural Network, Model Predictive Control, Convolutional Neural Network, Mobile Robot |
| Subjects: | 600 Technology (Applied sciences) > 621 Applied physics > 621.3 Electrical, magnetic, optical, communications, computer engineering; electronics, lighting > 621.38 Electronics, communications engineering > 621.381 Electronics |
| Divisions: | S2/S3 > Magister Teknik Elektro, Fakultas Teknik |
| Depositing User: | Endang Susworini |
| Date Deposited: | 20 Jul 2023 03:07 |
| Last Modified: | 20 Jul 2023 03:07 |
| URI: | http://repository.ub.ac.id/id/eprint/201963 |
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Text (DALAM MASA EMBARGO)
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