DETEKSI DAN PREDIKSI TRAJEKTORI OBJEK BERGERAK DENGAN OMNI-VISION MENGGUNAKAN PSO-NN DAN INTERPOLASI POLYNOMIAL - Novendra Setyawan; Nur Alif Mardiyah; Khusnul Hidayat (Universitas Muhammadiyah Ponorogo, 2019)
In the Indonesian wheeled soccer robot competition in one team consists of three robots, where one robot is a goalkeeper. In the competition the movement of robots and balls is very dynamic. So that a method is needed to predict the movement of the ball so that the goalkeeper can anticipate the movement of the ball. In this research the ball detected by digital image processing and Particle Swarm Optimization-Neural Network (PSO-NN) is used as a calibration model for object distance through omnidirectional cameras. The interpolation approach of the polynomial curve is used to obtain estimates of the model from two-dimensional data from detected objects. The results showed that the distance conversion in object detection with the PSO-NN model obtained 0.13% in percentage of average squared error (PMSE) measurement and 20% in an average prediction error.