Mail, Adam (2023) Implementasi Sistem Navigasi Mobile Robot Menggunakan Extended Kalman Filter dengan Fusion Sensor Rotary Encoder – Gyroscope - Lidar Scan Matching Interpolasi Iterative Closest Point. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sistem navigasi mengambil peranan penting dalam perkembangan otomatisasi robot dimana banyak peneliti, dunia industri, hingga institusi akademik mengambil peranannya dalam pengembangan sistem navigasi. Sistem ini memiliki tujuan utama untuk mendapatkan posisi robot secara tepat dan cepat dengan menggunakan perhitungan sensor yang digunakan. Dalam penerapannya, sistem navigasi terbagi menjadi empat tahapan yaitu data sensor, persepsi, perencanaan, dan kontrol. Data setiap sensor yang digunakan akan dibaca dan dikumpulkan. Data ini akan dijadikan sebagai persepsi robot. Tahapan perencanaan menggunakan informasi persepsi untuk perencanaaan tindakan selanjutnya. Hasil perencanaan akan diteruskan untuk digunakan sebagai kontrol komponen penggerak robot. Pada penelitian ini, implementasi yang dilakukan yaitu menggunakan mobile robot dengan roda mekanum. Integrasi sensor rotary encoder, gyroscope, dan lidar pada penelitian ini menggunakan Kalman filter. Sensor rotary encoder yang berjumlah dua dengan gyroscope diintegrasikan sehingga menghasilkan posisi global mobile robot terhadap lapangan dengan keluaran sumbu translasi x, sumbu translasi y, dan sudut φ. Hasil ini akan digunakan sebagai state tahap prediksi pada Kalman filter. Sensor lidar akan menjadi masukan kedalam sistem yaitu berupa map point cloud referensi lapangan dan data point cloud lidar. Data map point cloud referensi lapangan dan data point cloud lidar akan dibandingkan menggunakan algoritma Iterative Closest Point (ICP). Selanjutnya data lidar akan di interpolasi sehingga menghasilkan sampling yang lebih banyak. Didapatkan hasil implementasi sensor rotary encoder, gyroscope, dan lidar berupa hasil yang baik dimana rata-rata error sudut yaw diam yaitu 0.02° pada gyroscope, 0.19° pada ICP, dan 0.05° pada EKF. Hasil juga menunjukkan rata-rata error EKF dapat mengatasi akumulasi error yang dihasilkan sensor rotary encoder dan gyroscope.
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Navigation systems play an important role in the development of robot automation where many researchers, the industrial world, and even academic institutions take their roles in the development of navigation systems. This system has the main goal of getting the robot's position precisely and quickly by using the sensor calculations used. In its application, the navigation system is divided into four stages, namely sensor data, perception, planning, and control. Data for each sensor used will be read and collected. This data will be used as the robot's perception. The planning stage uses perceptual information for planning further actions. The results of the planning will be forwarded to be used as control for the robot driving components. In this study, the implementation was carried out by using a mobile robot with mechanical wheels. The integration of the rotary encoder, gyroscope, and lidar sensors in this study used the Kalman filter. Two rotary encoder sensors with a gyroscope are integrated so as to produce the global position of the mobile robot to the field with the output of the x axis of translation, the axis of translation of y, and the angle of φ. These results will be used as the state of the prediction stage in the Kalman filter. The lidar sensor will be input into the system in the form of a field reference point cloud map and lidar point cloud data. Field reference map point cloud data and lidar point cloud data will be compared using the Iterative Closest Point (ICP) algorithm. Furthermore, the lidar data will be interpolated to produce more sampling. The results obtained from the implementation of the rotary encoder, gyroscope, and lidar sensors were good results where the average yaw angle of rest error was 0.02° on the gyroscope, 0.19° on the ICP, and 0.05° on the EKF. The results also show that the average EKF error can overcome the accumulated errors generated by the rotary encoder and gyroscope sensors.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | ICP, Kalman Filter, Lidar, Mobile Robot, Sensor Fusion, ICP, Kalman Filter, Lidar, Mobile Robot, Sensor Fusion. |
| Subjects: | T Technology > TJ Mechanical engineering and machinery > TJ211 Robotics. T Technology > TJ Mechanical engineering and machinery > TJ211.415 Mobile robots |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Adam Mail |
| Date Deposited: | 02 Aug 2023 00:50 |
| Last Modified: | 02 Aug 2023 00:50 |
| URI: | http://repository.its.ac.id/id/eprint/100512 |
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