Skripsi

DESAIN AUTONOMOUS VEHICLE BERBASIS SENSOR FUSION DENGAN ALGORITMA HYBRID DEEP LEARNING DAN PATH PLANNING

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Penilaian

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Autonomous vehicles require various sensors that can support and fulfill their tasks to operate automatically. The integration of these sensors into a unified system is achieved through sensor fusion. However, most of the previous research has focused on sensor fusion for a single task. In this study, the focus is on sensor fusion with different tasks using deep learning to perform object-road detection and path planning using the A* algorithm. Real-time testing is conducted on two different routes, representing common conditions found in Indonesia. This sensor fusion system consists of a master microcontroller as the main controller and slave microcontrollers. The master microcontroller issues commands for motor movement or motor stop based on the data received, which includes results from path planning, object-road detection, rpm, steering angle, and magnetometer. Meanwhile, the slave microcontroller acts as an access point and data transmitter for various sensors, such as GPS, magnetometer, and encoder. During the first route testing, the autonomous vehicle encountered several collisions with road barriers. Therefore, in the second route testing, some parameters were adjusted, such as steering angle division and steering wheel PWM range, to address the issue. In each test, the autonomous vehicle was able to follow the road and the planned route. However, at certain route nodes, it was unable to reach them, requiring the intervention of the driver to manually control the autonomous vehicle and reach those nodes. Based on the test results, it is shown that sensor fusion with different tasks can be successfully implemented and operated in a third-level autonomous vehicle.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003801	T122991	T1229912023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1229912023

Publisher

Indralaya : Jurusan Teknik Elektro, Fakultas Teknik Universitas Sriwijaya., 2023

Collation

xvi, 66 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

681.207

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sensor
Jurusan Teknik Elektro

Specific Detail Info

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Statement of Responsibility

PITRIA

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