Deep learning applied to humanoid soccer robotics: playing without using any color information
Author:
Funder
ANID
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
http://link.springer.com/content/pdf/10.1007/s10514-021-09966-9.pdf
Reference33 articles.
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2. Andrew, A. (1979). Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters, 9(5), 216–219. https://doi.org/10.1016/0020-0190(79)90072-3.
3. Cruz, N., Lobos-Tsunekawa, K., & Ruiz-del Solar, J. (2018). Using convolutional neural networks in robots with limited computational resources: Detecting NAO robots while playing soccer. In H. Akiyama, O. Obst, C. Sammut, & F. Tonidandel (Eds.), RoboCup 2017: Robot World Cup XXI (pp. 19–30). Cham: Springer International Publishing.
4. Cruz, N., & Ruiz-del Solar, J. (2020). Closing the simulation-to-reality gap using generative neural networks: Training object detectors for soccer robotics in simulation as a case study. In The international joint conference on neural networks 2020
5. Felbinger, G. C., Göttsch, P., Loth, P., Peters, L., & Wege, F. (2019). Designing convolutional neural networks using a genetic approach for ball detection. In D. Holz, K. Genter, M. Saad, & O. von Stryk (Eds.), RoboCup 2018: Robot World Cup XXII (pp. 150–161). Cham: Springer International Publishing.
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Perception for Humanoid Robots;Current Robotics Reports;2023-11-28
2. TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021;Lecture Notes in Computer Science;2022
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