DOI : https://doi.org/10.62527/comien.2.4.71

Tracking System on Autonomous Vehicle using Radiolink M8N SE100 with Kalman Filter Method Based on Rasberry PI

Nabila Muti Karimah (1), Agus Trisanto (2), Nanang Rohadi (3), Herwanto (4)
(1) Department of Electrical engeneering, Universitas Padjadjaran, Jatinangor, Indonesia
(2) Department of Electrical engeneering, Universitas Padjadjaran, Jatinangor, Indonesia
(3) Department of Electrical engeneering, Universitas Padjadjaran, Jatinangor, Indonesia
(4) Faculty of Engeneering, Universitas Krisnadwipayana, Jakarta, Indonesia
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How to cite (COMIEN) :
Karimah , N. M., Trisanto , A., Rohadi , N., & Herwanto. (2025). Tracking System on Autonomous Vehicle using Radiolink M8N SE100 with Kalman Filter Method Based on Rasberry PI . International Journal on Computational Engineering, 2(4), 127–136. https://doi.org/10.62527/comien.2.4.71
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An autonomous vehicle is a robot that can move automatically toward the destination. This technology has the potential to change people's lifestyles, making future transportation systems safer, and can also be implemented for logistics delivery to areas that are difficult to reach. This final project aims to drive an autonomous vehicle to a specified destination using the Global Positioning System (GPS) and a compass sensor. Data from the compass sensor was used to determine the heading and bearing to determine the direction of motion of the autonomous vehicle. When testing the M8030 GPS in silent conditions for five minutes, the problems had an average measurement error of 4.78 meters. The Kalman Filter method was applied to minimize these inaccuracies, which is ideal for dynamic systems. The test results using the Kalman Filter with R=10 showed an average difference between the stop point and the target point of 7,285 meters, while the test results without the Kalman Filter showed a difference of more than 10 meters in each test. These results indicated that the Kalman Filter tracking system works well to reduce noise Then testing on the Compass QMC5883L sensor showed a small percentage of error with an average of 0.36%. In future research, it can use other GPS modules with more robust signal locking because autonomous vehicles need accuracy and precision. It is possible to increase the performance of the autonomous vehicle by adding a controller to the system.

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