Implementation of high-voltage kicker system for “ROSTU” middle-size league robot soccer

Middle-size robot soccer is one of the divisions that competed in national events such as the National Indonesia Robotics Competition and international competitions such as the middle size league (MSL). One of the main components in soccer robots is the kicker system. The kicker system is expected to be high torque, robust, and safe. In this work, a high voltage kicker system is designed and evaluated to substitute ROSTU's previous kicker system. This high voltage solenoid-based kicker system works at 380V and uses the electromagnetic force principle to move a ball. The performance criteria of the kicker system are it can move a ball with a mass of around 1 kg for a minimum range of 3 m and control the charging and discharging process in high voltage conditions. The experiment results show that the kicker system can move a ball with a mass of 1.06 kg, a difference kick distance from 100cm to 350cm, and a monitoring system that can show information about the capacitor voltage and system status.

Real-Time Evaluation Algorithm of Human Body Movement in Football Training Robot

As one of the most challenging topics in the field of artificial intelligence, soccer robots are currently an important platform for humanoid robotics research. Its fields cover a wide range of fields, including robotics, artificial intelligence, and automatic control. Kinematics analysis and action planning are the key technologies in the research of humanoid soccer robots and are the basis for realizing basic actions such as walking. This article mainly introduces the real-time evaluation algorithm of human motion in the football training robot. The football robot action evaluation algorithm proposed here designs the angle and wheel speed of the football robot movement through the evaluation of the angular velocity and linear velocity of the center of mass of the robot. The overall system of the imitation human football robot is studied, including the mechanical system design. The design of the leg structure, the decision-making system based on the finite state machine, the robot vision system, and the image segmentation technology are introduced. The experimental results in this article show that the action of the football training robot model is very stable, the static rotation movement time is about 220 ms, and the fixed-point movement error is less than 1 cm, which fully meets the accuracy requirements of the large-space football robot.

Sistem Kontrol Robot Sepak Bola Beroda menggunakan Finite State Machine (FSM)

ABSTRAKPerkembangan teknologi menuntut transisi alat yang berbasis manual menjadi otomatis. Hal ini tak terkecuali pada perkembangan robot, termasuk robot sepak bola. Robot ini mengalami perkembangan sangat pesat. Hal ini didukung dengan adanya Kontes Robot Sepak Bola Indonesia (KRSBI). Penelitian kali mengusulkan robot sepak bola beroda dengan omni-directional weel sebagai pergrakannya menggunakan Finite State Machine (FSM) sebagai metode kontrol untuk menjalankan misi yang ditentukan. Misi robot yang dikembangkan yaitu mencari bola (wander), mencari posisi lurus terhadap bola, mencari gawang, dan menendang bola. Robot ini menggunakan CMUCam5 sebagai sensor masukan untuk mendeteksi bola sebagai target bedasarkan warna dan jarak. Jarak yang digunakan yaitu antara 20 cm sampai 60 cm dengan sudut antara -40 derajat sampai 40 derajat dan intensitas cahaya antara 113 sampai 1213 lux. Implementasi FSM pada robot sepak bola beroda dengan 5 jenis percobaan mencapai tingkat keberhasilan yang bagus yaitu 86% dengan rata-rata waktu menjalankan misi 29.24 detik.Kata kunci: CMUCam5, Finite State Machine, Omni-directional wheel, Robot Sepak Bola ABSTRACTTechnological development demands a transition from manual to automatic tools. It includes soccer robots that have developed so rapidly. This development is supported by the Indonesian Football Robot Contest (KRSBI). This study proposes a wheeled soccer robot with Omni-directional weel as its movement using Finite State Machine (FSM) as a control to carry out the specified mission. The mission is wandering to seek the ball, looking for a straight position to the ball, looking for the goal, and kicking the ball. This robot uses CMUCam5 as an input to detect the ball as a target based on color and distance. The distance used is between 20-60 cm with an angle of -40 degree to 40 degree and light intensity between 113-1213 lux. The implementation of FSM on a wheeled soccer robot with five types of experiments achieved a good result of 86% with an average mission time of 29.24 seconds.Keywords: CMUCam5, Finite State Machine, Omni-directional weel, Wheeledsoccer robot

Intercept Algorithm for Predicting the Position of Passing the Ball on Robot Soccer ERSOW

ERSOW is the name of a wheeled soccer robot that competes in the Kontes Robot Sepak Bola Indonesia (KRSBI). The soccer robot plays a soccer game based on the rules adapted from the human soccer game. The ERSOW team was formed in 2016. Starting in 2017, ERSOW participated in the KRSBI with the Middle Size League (MSL) type. Research in the field of wheeled soccer robots is mostly carried out on robot intelligence, such as how robots detect and look for balls, dribble, pass the ball, avoid opponents, and communicate in teams. This research focuses on the ability that the robot can pass the ball in KRSBI 2020. There are adjustments to the rules for its implementation online where the robot has to pass the ball and score as many goals as possible. The robot's ability to know the direction of ball movement and cut the ball movement or intercept is needed. By utilizing data processing from vision to obtain ball speed data and speed algorithm calculations, the passing ball method has a small chance of missing. Based on the results of experiments that have been carried out, the success of ERSOW in passing using this method is 94.7%.

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

Goalpost Detection Using Omnidirectional Cameras on ERSOW Soccer Robots

The ERSOW robot is a soccer robot developed by Politeknik Elektronika Negeri Surabaya, Indonesia. One important ability of a soccer robot is the ability to find the goal in the field. Goal Post is often used as a sign by soccer robots in a match. The mark is a reference robot in the field to be used in determining the strategy. By knowing the location of the goal in a field, the soccer robot can make the decision to maneuver in the match to get the right goal kick. There are various methods of detecting goal. One of them is to detect goal post using vision. In this study the radial search lines method is used to detect the goalposts as markers. Image input is generated from an omnidirectional camera. The goal area that is detected is the front side of the goal area. With experiments from 10 robot position points in the field, only 1 position point cannot detect the goal. The robot cannot detect the goal because what is seen from the camera is the side of the goal, so the front side of the goal area is not visible.Keywords— omnidirectional camera, vision, radial search lines, goal detection, ersow soccer robot

Goalpost Detection Using Omnidirectional Cameras on ERSOW Soccer Robots

The ERSOW robot is a soccer robot developed by Politeknik Elektronika Negeri Surabaya, Indonesia. One important ability of a soccer robot is the ability to find the goal in the field. Goal Post is often used as a sign by soccer robots in a match. The mark is a reference robot in the field to be used in determining the strategy. By knowing the location of the goal in a field, the soccer robot can decide to maneuver in the match to get the right goal kick. There are various methods of detecting goals. One of them is to detect goal posts using vision. In this study, the radial search lines method is used to detect the goalposts as markers. Image input is generated from an omnidirectional camera. The goal area is detected on the front side of the goal area. With experiments from 10 robot position points in the field, only 1 position point cannot detect the goal. The robot cannot detect the goal because what is seen from the camera is the side of the goal, so the front side of the goal area is not visible.