Integrasi Data Multisensor Untuk Peningkatan Lokalisasi Pada Navigasi Mobile Robot Menggunakan Extended Kalman Filter

Ramadhan, Fadhly (2025) Integrasi Data Multisensor Untuk Peningkatan Lokalisasi Pada Navigasi Mobile Robot Menggunakan Extended Kalman Filter. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Sistem navigasi robot tidak terlepas dari penggunaan sensor yang digunakan sebagai masukan untuk menentukan persepsi robot baik secara internal maupun eksternal. Setiap sensor memiliki kelebihan dan kekurangannya masing – masing, seperti penggunaan sensor INS menawarkan keunggulan dalam menyediakan data posisi dan orientasi dengan laju tinggi, sementara rotary encoder menyediakan data posisi yang lebih akurat namun rotary encoder yangsensitif terhadap slip. Mengandalkan satu jenis sensor sering kali tidak cukup untuk mencapai estimasi posisi yang akurat dan andal, terutama dalam lingkungan yang kompleks dan dinamis. Kombinasi kedua sistem ini melalui EKF memungkinkan pemanfaatan keunggulan masing-masing sistem, menghasilkan data navigasi yang lebih stabil dan akurat. Dalam penelitian ini, Sensor yang digunakan meliputi Inertial Measurement Unit (IMU) untuk memperoleh orientasi, Rotary Encoder untuk posisi translasi, dan RPLidar A1 untuk deteksi lingkungan. Proses integrasi data melibatkan mekanisasi IMU, filtering menggunakan Kalman Filter, dan pemetaan lingkungan SLAM. Mobile robot yang digunakan memiliki konfigurasi roda omni-directional 4WD yang memungkinkan gerakan bebas dalam ruang dua dimensi. Hasil pengujian menunjukkan bahwa integrasi multisensor dengan EKF signifikan meningkatkan akurasi estimasi posisi dan orientasi dibandingkan metode sensor tunggal.
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The robot navigation system cannot be separated from the use of sensors which are used as input to determine the robot's perception both internally and externally. Each sensor has its own advantages and disadvantages, such as the use of the INS sensor offers the advantage of providing position and orientation data at a high rate, while the rotary encoder provides more accurate position data but the rotary encoder is sensitive to slip. Relying on one type of sensor is often insufficient to achieve accurate and reliable position estimation, especially in complex and dynamic environments. The combination of these two systems via EKF allows exploiting the advantages of each system, producing more stable and accurate navigation data. In this research, the sensors used include the Inertial Measurement Unit (IMU) to obtain orientation, the Rotary Encoder for translation position, and the RPLidar A1 for environmental detection. The data integration process involves IMU mechanization, filtering using the Kalman Filter, and SLAM environment mapping. The mobile robot used has a 4WD omni-directional wheel configuration which allows free movement in two-dimensional space. Test results show that multisensor integration with EKF significantly increases the accuracy of position and orientation estimation compared to single sensor methods.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Extended Kalman Filter, Lokalisasi, Sensor Fusion, Mobile Robot, Navigasi, Localization, Navigation
Subjects:	T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.62 Simulation T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > T Technology (General) > T57.83 Dynamic programming T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.546 Computer algorithms
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Fadhly Ramadhan
Date Deposited:	30 Jan 2025 01:37
Last Modified:	30 Jan 2025 01:37
URI:	http://repository.its.ac.id/id/eprint/117061

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