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Journal : JAREE (Journal on Advanced Research in Electrical Engineering)

Depth Image Assisted Aiming for Scoring Goal in Wheeled Soccer Robot Kusuma, Hendra; Samhan, Dzulfikar Ahmad; Tasripan, Tasripan; Dikairono, Rudy
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 2 (2024): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i2.268

Abstract

Wheeled soccer robot, as an automatic robot, required to have an advanced decision making system based on information it grasp from its surrounding. One of the most crucial decision making ability is to determine aiming angle when it is scoring goal.This research will enhance the aiming ability for scoring goal by predicting unguarded area of goal. Combination of depth image and RGB image information will be used to predict the position of unguarded space in goal. This position will be converted into aiming angle for the robot. Intel Realsense D435i depth camera will be used to get RGB and depth image simultaneouslyBy using this method, the system is capable to predict unguarded area in all of 60 test points, with 1.3% average error for the predicted coordinate.
Deep Neural Network for Visual Localization of Autonomous Car in ITS Campus Environment Dikairono, Rudy; Kusuma, Hendra; Prajna, Arnold
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 7, No 2 (2023): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v7i2.365

Abstract

Intelligent Car (I-Car) ITS is an autonomous car prototype where one of the main localization methods is obtained through reading GPS data. However the accuracy of GPS readings is influenced by the availability of the information from GPS satellites, in which it often depends on the conditions of the place at that time, such as weather or atmospheric conditions, signal blockage, and density of a land. In this paper we propose the solution to overcome the unavailability of GPS localization information based on the omnidirectional camera visual data through environmental recognition around the ITS campus using Deep Neural Network. The process of recognition is to take GPS coordinate data to be used as an output reference point when the omnidirectional camera takes images of the surrounding environment. Visual localization trials were carried out in the ITS environment with a total of 200 GPS coordinates, where each GPS coordinate represents one class so that there are 200 classes for classification. Each coordinate/class has 96 training images. This condition is achieved for a vehicle speed of 20 km/h, with an image acquisition speed of 30 fps from the omnidirectional camera. By using AlexNet architecture, the result of visual localization accuracy is 49-54%. The test results were obtained by using a learning rate parameter of 0.00001, data augmentation, and the Drop Out technique to prevent overfitting and improve accuracy stability.