Object Detection for Robot Coordination in Robotics Soccer
Keywords:
Artificial Neural Networks, Deep learning, Multi-agent, object detection, RoboticsAbstract
In June 2018, iCog Labs held its second annual robosoccer competition which featured groups of humanoid robots playing soccer against each other. The authors were members of a team called upon to represent Nigeria with the University of Lagos at the competition which took place in Ethiopia. The work here presents a review of the approach taken to address the problem of automating robot coordination in real-world soccer applications. The design methodology relies on the Robot Operating System (ROS) as the platform upon which an asynchronous communication network between each robot and a central server is built. On the network, each robot is a node that consists of sub nodes for object detection and motion control. For object detection the work makes use of the you only look once (YOLO)v2 deep learning algorithm, and a simple decision-making algorithm for controlling vcv the robot based on the objects detected is devised. To quantify the object detection results, the common objects in context (COCO) evaluation metric is used. The results indicate an average recall and precision of 84% across different IOU. For qualitative results on the robot coordination in the ball’s direction, a reference to the open-source implementation of the work has been provided.
References
iCog Labs (2018). iCog Makers Robosoccer cup Rules and Regulations. Available online at: https://icog-labs.com/afrobocup_rules_regulations_2018.pdf. Accessed on January 3, 2021.
Behnke, S., J. Müller and M. Schreiber. (2005). Toni: A soccer playing humanoid robot. In Robot Soccer World Cup, 59-70 Springer: Heidelberg, Berlin.
Bochkovskiy, A.; C. Y. Wang and H. Y. M. Liao. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, Available online at: https://arxiv.org/abs/2004.10934. Accessed on November 13, 2021.
Ferrein, A. and Steinbauer, G. (2016). 20 Years of RoboCup. Künstl Intell, 30: 225–232.
Jin, X.; Z. Yang; H. Wang; J. Yin; C. Wang and F. Wu. (2020). Improvements of SYCU Humanoid Robot. In Journal of Physics, Conference Series 1693(1): 012204.
LeCun, Y.; Y. Bengio and G. Hinton. (2015). Deep learning. Nature. May; 521 (7553): 436-444.
LeCun, Y.; D. Touresky, G. Hinton and T. Sejnowski. (1988). A theoretical framework for back-propagation. Proceedings of the 1988 connectionist models summer school, CMU, Pittsburgh, Pa: Morgan Kaufmann, (1): 21-28.
Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: Past, present, and future. Journal of cognitive neuroscience, 33(10), 2017-2031.
Lin, T.Y; M. Maire; S. Belongie; J. Hays; P. Perona; D. Ramanan; P. Dollár and C. L. Zitnick. (2014) Microsoft coco: Common objects in context. Presented at the European conference on computer vision, 740-755, Springer, Cham.
Redmon, J., and Farhadi A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7263-7271 IEEE:USA.
Redmon, J.; S. Divvala; R. Girshick and A. Farhadi. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788 IEEE:USA.
Repository (2022) Robosapien Object Detection using Darkflow. Available online at https://github.com/ogbanugot/Robosapien-Object-Detector-using-Darkflow. Accessed on February 15, 2022.
Schmidt, R. M.; F. Schneider and P. Hennig. (2021). Descending through a crowded valley-benchmarking deep learning optimizers. Paper presented at the International Conference on Machine Learning 9367-9376 PMLR:USA.
Spensieri, D; E. Åblad; R. Bohlin; J.S. Carlson and R. Söderberg (2021). Modeling and optimization of implementation aspects in industrial robot coordination. Robotics and Computer-Integrated Manufacturing, (69):102097.
Wang, D; H. Deng and Z. Pan (2020). Mrcdrl: Multi-robot coordination with deep reinforcement learning. Neurocomputing, (406): 68-76.
Zhang, A.; Z. C. Lipton; M. Li and A. J. Smola. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342. Available online at: https://arxiv.org/abs/2106.11342, Accessed on November 13, 2021.
Zhou, D. X. (2020). Theory of deep convolutional neural networks: Downsampling. Neural Networks, (124): 319-327.
Additional Files
Published
Issue
Section
License
Copyright (c) 2022 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approve the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form should be downloaded from the NJTD Website ( http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
