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Automatic Guided Vehicle (AGV) is one type of robot that works following a path. In this study, AGV is used in campus or office environments. Campus environment causes the passage path is difficult to be conditioned in sterile conditions because of many obstacles. This makes the robot must have an obstacle detection system that is able to classify between static and moving obstacles. This research use trash bin as static obstacles and human as moving obstacles. To detect the obstacles, this research use haar-cascade as a rough search and local binary pattern as a fine search. Haar-cascade is made by opencv haar training. Training was done using 300 positive images and 2317 negative images on each object. Haar-cascade classifier is obtained after training up to 10 stages. Haar-cascade was tested at a distance below 4 meters from the object. A fine search using a local binary pattern is performed when the haar-cascade detects more than one object. From the tests that have been done, the system managed to detect an obstacle with the success rate of 81,7%

PEMBUATAN HAAR-CASCADE DAN LOCAL BINARY PATTERN SEBAGAI SISTEM PENDETEKSI HALANGAN PADA AUTOMATIC GUIDED VEHICLE