IMPLEMENTASI TEKNOLOGI COMPUTER VISION SEBAGAI PENGENDALI MOBILE ROBOT BERBASIS KAMERA WEB
DOI:
https://doi.org/10.30736/jti.v2i2.72Keywords:
Canny Edge Detection, Closed Environment, Autonomous Navigation, OpenCV, Raspberry pi, Mobile RobotAbstract
In this research will be developed autonomous Mobile Robot navigation system, using vision sensor in the form of web camera. The ability of the robot to find the path, avoiding obstacles in an indoor environment becomes the key to the success of navigation. One of the things that underlies the robot navigation system is the process of image information processing from a web camera. So it takes a method that can process image information from the web camera into image data more easily read by the computer. The method that can be used to solve this problem is Canny Edge Detection. Canny Edge Detection has some of the most optimum edge detection criteria that localize the image well, detect objects well and clear response. With these advantages, the Canny Edge Detection method can produce a more representative image approaching the real object. After the edge detection process is completed then the next step is to identify and identify paths or obstacles that exist. Paths and obstructions that have been identified will be used as models to determine which direction the robot will run. The whole process of computing and control will be done using Raspberry pi, while for image processing using OpenCV application.Downloads
References
Awaluddhin Choliq Azis, Widya Cahyadi, Catur Suko Sarwono, 2015, Aplikasi Tracking Object pada Sistem Web Streaming dengan Protokol TCP/IP sebagai Sistem Navigasi Mobile Robot Berbasis Mini PC, elektronik Jurnal Arus Elektro Indonesia, Universitas Jember
Kukuh Darmawan Setyanto, Ike Fibriani, Sumardi, 2016, Pengendalian Mobile Robot Vision Menggunakan Webcam Pada Objek Arah Panah Berbasis Raspberry Pi, Jurnal Arus Elektro Indonesia, Universitas Jember
Lasti Warasih H. , 2008, Perancangan Mobile Robot Dengan Sensor Kamera Menggunakan Sistem Kendali Fuzzy, Teknik Elektro Universitas Indonesia.
Barcelo G.C, Panahandeh G, Jansson M, 2013, Image-Based Floor Segmentation in Visual Inertial Navigation, KTH Royal Institut of Technology
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