Ball Detection using Local Binary Pattern in Middle Size Robot Soccer (ERSOW)

Wibowo, Iwan Kurnianto; Haq, Muhammad Abdul; Bachtiar, Mochamad Mobed; Dewantara, Bima Sena Bayu; Ihsan, Fadl Lul Hakim

doi:10.1109/ic2ie47452.2019.8940835

Cited by 13 publications

(10 citation statements)

References 16 publications

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“…Before we did this research, we had researched ball detection using local binary pattern (LBP) [7]. However, the output provided is not satisfactory, for example, the FPS value on ball detection using the LBP method produces about 20 fps when used for a long time.…”

Section: Testing Of Ball and Obstacle Trackingmentioning

confidence: 99%

“…In Indonesia, there is a similar competition, namely the Indonesian Wheeled Robot Soccer Contest, where the rules are the same as the RoboCup middle size league taking into account conditions and developments in Indonesia such as field size and other factors [6]. Robot soccer is a robot that is designed to be able to play soccer autonomously [7]. So, the robots can play like a soccer game in general, soccer robots must be equipped with features that can later support the running of the robot then the robot can make decision-making conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The related works that have been done so far are as follows, ERSOW is a robot team from Politeknik Elektronika Negeri Surabaya (PENS) that competes in the Indonesia robot soccer contest wheeled [6], [7]. The conditions of the obstacle detection system have a processing speed of 15 fps and the ball detection system still has a processing speed of 24 fps, while the speed of the ERSOW robot is 2 m/s [7]. CAMBADA is the robocup middle size league (MSL) soccer team of the University of Aveiro, Portugal [3].…”

Section: Introductionmentioning

confidence: 99%

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Improving the speed of ball detection process and obstacle detection process in ERSOW robot using omnidirectional vision based on ROS

Haq

Wibowo

Dewantara

2021

IJEECS

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This paper presents a novel approach for improving the computation speed of the ball detection and obstacle detection processes in our robot. The conditions of obstacle detection and ball detection in our robot still have a slow processing speed, this condition makes the robot not real-time and the robot's movement is hampered. To build a good world model, things to note are obstacle information and real-time ball detection. The focus of this research is to increase the speed of the process of the ball and obstacle detection around the robot. To increase the speed of the process, it is necessary to optimize the use of the OpenCV library on the robot operating system (ROS) system to divide the process into several small processes so that the work can be optimally divided into threads that have been created. Then, minimize the use of too many frames. This information will be sent to the base station and also for obstacle avoidance purposes.

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Section: Testing Of Ball and Obstacle Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving the speed of ball detection process and obstacle detection process in ERSOW robot using omnidirectional vision based on ROS

Haq

Wibowo

Dewantara

2021

IJEECS

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“…The way to detect it is to use a camera. ERSOW uses omnidirectional cameras as a vision to see the entire field area [1] [2].…”

Section: Introductionmentioning

confidence: 99%

Goalpost Detection Using Omnidirectional Cameras on ERSOW Soccer Robots

Bachtiar

Wibowo

Hamarsudi³

2020

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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.

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“…Sistem zoom otomatis dapat digunakan untuk menangkap objek kecil dan melacak objek menggunakan filter Kalman [10]. Pengujian sistem dilakukan seperti melihat pengaruh pencahayaan pada kamera omnidirectional [11] dengan pendeteksian bola pada robot dapat dikembangkan dengan mendeteksi pola biner berbasis sistem embedded dan merujuk pada sistem robot CAMBADA [12]- [15]. Sistem koordinasi gerakan robot beserta pelacakan objek dikembangkan dari penelitian tentang robot cerdas [16]- [21].…”

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Navigasi Robot Penjaga Gawang Berdasarkan Prediksi Posisi dan Waktu Kedatangan Bola

Setiawardhana¹,

Dikairono

Purwanto

et al. 2020

JNTETI

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Penelitian tentang robot sepakbola beroda telah banyak dikembangkan, terutama untuk proses akurasi pendeteksian dan klasifikasi objek. Makalah ini bertujuan untuk menyelesaikan permasalahan akurasi prediksi posisi bola dan waktu kedatangan di gawang serta navigasi robot untuk memblokade bola agar tidak melintasi gawang. Penelitian sebelumnya menggunakan metode Single Layer Neural Network (SLNN) dan Two Layer Neural Network (TLNN). Makalah ini membuat algoritme Modified Two-Layer Neural Network (MTLNN) untuk peningkatan akurasi prediksi posisi dengan waktu kedatangan bola dan Goalkeeper Robot Navigation (GKRN) untuk navigasi blokade bola. Algoritme dibuat dengan modifikasi arsitektur TLNN di jumlah node menjadi delapan input dan dua output, dengan jumlah hidden yang didesain sesuai kebutuhan. Akurasi dari prediksi sangat memengaruhi keberhasilan blokade bola karena digunakan sebagai referensi target tujuan akhir dari robot. Algoritme GKRN dibuat dengan memodifikasi fungsi keanggotaan Fuzzy Inferences System (FIS) yang disesuaikan dengan kebutuhan robot. Hasil pengujian menunjukkan peningkatan akurasi prediksi dari metode sebelumnya, TLNN dan SLNN, hingga dua puluh kali lebih baik untuk posisi bola di gawang dan empat kali lebih baik untuk waktu kedatangan bola ke gawang. Secara keseluruhan, sistem navigasi memperoleh keberhasilan memblokade bola di gawang sebesar 90%.

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Ball Detection using Local Binary Pattern in Middle Size Robot Soccer (ERSOW)

Cited by 13 publications

References 16 publications

Improving the speed of ball detection process and obstacle detection process in ERSOW robot using omnidirectional vision based on ROS

Improving the speed of ball detection process and obstacle detection process in ERSOW robot using omnidirectional vision based on ROS

Goalpost Detection Using Omnidirectional Cameras on ERSOW Soccer Robots

Navigasi Robot Penjaga Gawang Berdasarkan Prediksi Posisi dan Waktu Kedatangan Bola

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