- Article
- Open access
- Published:
Scientific Reports (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Abstract
Real-time detection of robot position and orientation is a critical challenge in multi-robot autonomous soccer systems. This paper presents two complete algorithms for position and orientation determination of multiple RoboCup robots from overhead camera images. In the first algorithm, blob analysis with HSV color segmentation is used for position detection, and two newly designed lightweight CNN architectures (RoboCup-I and RoboCup-II) are used for orientation estimation of single-color robots. In the second algorithm, SSD-MobileNetV2 and RFB-ULGFD object detection models are compared for robot and ball position detection, while a novel color segmentation-based trigonometric orientation algorithm is proposed for multi-robot orientation detection. Experimental results show that the RFB-ULGFD model with 480 × 360 input achieving a mean Average Precision (mAP) of 98.17% at IoU 0.5 with a model inference time of only 4 ms on GPU (12 ms including preprocessing and post-processing within the full pipeline). The proposed color-segmentation-based orientation algorithm achieves a Mean Squared Error (MSE) of 2.06 degrees on synthetic data with a total pipeline latency of only 6–15 ms even in CPU environments, satisfying the demands of real-time multi-robot systems.
Subjects
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics
Not applicable. This study did not involve human participants, human data, or human tissue.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Özkurt, A., Öztekin, A.E. Real-time position and orientation determination of multiple robots in Robocup soccer using lightweight deep learning models. Sci Rep (2026). https://doi.org/10.1038/s41598-026-65913-7
Download citation
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-65913-7