Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique

Authors

  • Danis Bagus Setiawan Shipbuilding Institute of Polytechnic Surabaya
  • Agus Khumaidi Shipbuilding Institute of Polytechnic Surabaya
  • Projek Priyonggo Shipbuilding Institute of Polytechnic Surabaya
  • Mohammad Basuki Rahmat Shipbuilding Institute of Polytechnic Surabaya
  • Imam Sutrisno Shipbuilding Institute of Polytechnic Surabaya
  • Khoirun Nasikhin Shipbuilding Institute of Polytechnic Surabaya
  • Adi Wisnu Sahputera Shipbuilding Institute of Polytechnic Surabaya

DOI:

https://doi.org/10.33086/atcsj.v2i1.1204

Keywords:

trigonometric technique, prediction, direction, ball, wheeled soccer robot goalkeeper

Abstract

In this research Trigonometry Technique was implemented to predict the ball movement direction for Wheeled Soccer Robot Goalkeeper. The performance of goalkeeper robot in Wheeled Soccer Robot Contest is very important. The crucial problem with goalkeeper robot is the delay in ball detection by the camera because the results of the camera images captured are always slower than the pictures that have been captured. This causes the robot's response to blocking the opponent's kick ball being late. Trigonometry Technique is one technique that can be used to predict the direction of the ball movement based on trigonometry mathematical formulas. The input data used is the location of the last ball position (x–last ball and y-last ball) and the location of the current ball position (x-current ball and y-current ball). The outputs are the prediction of the next ball location (x-predict ball and y-predict ball) and the prediction of ball movement direction prediction. The results are the goalkeeper's robot successfully predicts the opponent's kick direction with 90% accuracy and can predict the location of the next ball very well. By implementing this method, it is expected to optimize the performance of the goalkeeper robot in saving the goal.

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References

[1] Al-Ammri, A. Salam, and Iman Ahmed. Control of Omni-Directional Mobile
Robot Motion. Al-Khwarizmi Engineering Journal 6 (2010): 1-9.
[2] Beham, M. Parisi, and A. B. Gurulakshmi. Morphological Image Processing
Approach on the Detection of Tumor and Cancer Cells. 2012 International
Conference on Devices, Circuits and Systems (ICDCS). Coimbatore. India, 2012.
[3] Budianto, Ahmad, et al. Analysis of Artificial Intelligence Application Using Back
Propagation Neural Network and Fuzzy Logic Controller on Wall-Following
Autonomous Mobile Robot. 2017 International Symposium on Electronics and
Smart Devices. 2017: 62-66
[4] Hidayatullah, Priyanto. Pengolahan Citra Digital (Teori dan Aplikasinya).
Bandung: Informatika. 2017.
[5] Indrasutanto, Tjondro, and Tanti Yunitasari. Pendayagunaan Linear Air Track
untuk Percobaan Gerak Lurus Beraturan dan Gerak Lurus Berubah Beraturan.
Magister Scientiae. no. Edisi No. 26 (2009): 98-122.
[6] Khumaidi, Agus, Eko Mulyanto Yuniarno, and Mauridhi . Welding Defect
Classification Based on Convolution Neural Network (CNN) and Gaussian Kernel.
2017 International Seminar on Intelligent Technology and Its Applications
(ISITIA). 2017: 261-265.
[7] Khumaidi, Agus. Implementasi Pengolahan Video dengan Metode Color
Threshold pada Prototype Kapal Pendeteksi Korban Kecelakaan Laut Berbasis
Android. Surabaya. 2015.
[8] Kurniawan, D A, et al. Comparison of Extreme Learning Machine and Neural
Network Method on Hand Typist Robot for Quadriplegic Person. 2017
International Symposium on Electronics and Smart Devices. 2017: 101-106.
[9] Marzuqi, Irfan, Gilang Prilian Arinata, Zindhu Maulana Ahmad Putra, and Agus
Khumaidi. Segmentasi dan Estimasi Jarak Bola dengan Robot Menggunakan
Stereo Vision. 5th Indonesian Symposium on Robotic Systems and Control.
Bandung. 2017.
[10] Mukherjee, Aroop, and Soumen Kanrar. Enhancement of Image Resolution by
Binarization. International Journal of Computer Applications. 2010: 15-19.
[11] Nazar, A, et al. Quality Control of Cigarettes Packaging using Convolutional
Neural Network. The 1st International Conference on Advanced Engineering and
Technology. 2009: 012002.
[12] Puneet, and Naresh Kumar Garg. Binarization Techniques used for Grey Scale
Images. International Journal of Computer Applications (0975 – 8887) 71 (2013):
8-11.
[13] RISTEKDIKTI. Buku Panduan Kontes Robot Sepakbola Indonesia Divisi Beroda
(KRSBI Beroda) 2019. Jakarta. 2018.
[14] Sudin, Muhammad Nuruddin, Siti Norul Huda Sheikh Abdullah, Mohammad
Faidzul Nasrudin, and Shahnorbanum Sahran. Trigonometry Technique fot Ball
Prediction in Robot Soccer. Robor Intelligence Technology and Apllication.
Switzerland: Springer International Publishing. 2014

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Published

2019-09-07

How to Cite

Bagus Setiawan, D., Khumaidi, A., Priyonggo, P., Basuki Rahmat, M., Sutrisno, I., Nasikhin, K., & Wisnu Sahputera, A. (2019). Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique. Applied Technology and Computing Science Journal, 2(1), 39–51. https://doi.org/10.33086/atcsj.v2i1.1204

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Articles