PEMANFAATAN METODE WAVELET PADA ROBOT SEPAKBOLA BERBASIS MACHINE LEARNING GOOGLE TENSORFLOW
Abstract
Humanoid Football robot that will be investigated is a wheeled model developed with the ability to predict or prediction in a football field with high accuracy and image resolution. This robot is expected to be able to keep the goal for up to 25 minutes with a cruising reach of 100 m. Also, the robot can monitor or monitor the desired area so that it can keep the goal from attacks from the opposing robot. This robot is expected to complete prediction missions towards the ball and autonomous monitoring without being controlled by the pilot. Robot control is carried out by a Ground Control Station (GCS) computer. The goalkeeper's robot process uses Google TensorFlow's machine learning technology that is integrated with the wavelet method to enable this robot to keep the goalposts from the area effectively and efficiently.
Keywords: robot, machine learning, prediction, Ground Control Station, wavelet method
Full Text:
PDFReferences
N. Muhammad, D. Fofi and S. Ainouz, “Current state of the art of vision based SLAM,†in Proceedings of the SPIE, California, 2009, pp. 72510F–72510F–12.
J. AULINAS, Y. PETILLOT, J. SALVI, and X. LLADÓ, “The SLAM problem: a survey,†in Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence, Costa Brava, 2008, pp. 363–371.
R. Triebel and W. Burgard, “Improving simultaneous mapping and localization in 3D using global constraints,†in Proceedings of the 20th national conference on Artificial intelligence, Pennsylvania, 2005, pp. 1330–1335.
J. J. Leonard and H. F. Durrant-Whyte, “Simultaneous map building and localization for an autonomous mobile robot,†in Proc. IEEE Int. Workshop on Intelligent Robots and systems, Osaka, 1991, pp.1442– 1447.
D. Filliat and J. A. Meyer, “Map-based navigation in mobile robots. i. a review of localization strategies,†Journal of Cognitive Systems Research, vol. 4, no. 4, pp. 243–282, Dec. 2003.
J. J. Leonard and H. F. Durrant-Whyte, “Mobile robot localization by tracking geometric beacons,†IEEE Trans. Robotics and Automation, vol. 7, no. 3, pp. 376–382, Jun. 1991.
Google. Tensorflow.
Internet: https://www.TensorFlow.org. diakses tanggal 6 Februari 2020
Ó. M. Mozos, A. Gil, M. Ballesta, and O. Reinoso, “Interest point detectors for visual SLAM,†in 12th Conference of the Spanish Association for Artificial Intelligence, Salamanca, pp. 170–179, Nov. 2007.
C. Harris, M. Stephens, “A combined corner and edge detector,†in Proceedings of the Fourth Alvey Vision Conference, Manchester, 1988, pp. 147–151.
H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: speeded up robust features,†in Proceedings of the 9th European Conference on Computer Vision, Graz, 2006, pp. 404–417.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,†International Journal on Computer Vision, vol. 60, no. 2, pp. 91–110, Jan. 2004.
DOI: https://doi.org/10.37058/jeee.v1i2.1552
Refbacks
- There are currently no refbacks.
< p>