We present our open humanoid robot platform Wolfgang. The described hardware focuses on four aspects. Firstly, the robustness against falls is improved by integrating 3D printed elastic elements. Additionally, a high control loop frequency is achieved by using new custom control electronics. Furthermore, a torsion spring is applied to reduce the torque on the knee joints. Finally, computational power is provided through the combination of different processors. The paper also presents the ROS-based software stack that is used in RoboCup.

Wolfgang-OP: A Robust Humanoid Robot Platform for Research and Competitions

Soccer Field Boundary Detection Using Convolutional Neural Networks

AbstractWe present a dataset specifically designed to be used as a benchmark to compare vision systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection of images taken in various real-world locations as well as a collection of simulated images. It enables comparing vision approaches with a meaningful and expressive metric. The contributions of this paper consist of providing a comprehensive and annotated dataset, an overview of the recent approaches to vision in RoboCup, methods to generate vision training data in a simulated environment, and an approach to increase the variety of a dataset by automatically selecting a diverse set of images from a larger pool. Additionally, we provide a baseline of YOLOv4 and YOLOv4-tiny on this dataset.KeywordsComputer visionVision datasetDeep learning 

TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021

We showcase a pipeline to train, evaluate, and deploy deep learning architectures for monocular depth estimation in the RoboCup Soccer Humanoid domain. In contrast to previous approaches, we apply the methods on embedded systems in highly dynamic but heavily constrained environments. The results indicate that our monocular depth estimation pipeline is usable in the RoboCup environment.

Applying Monocular Depth Estimation in RoboCup Soccer

In this paper, we present Shotifier, a binary classification pipeline based on the principle of hybrid parallelism. Shotifier pipeline focuses on forwards or strikers, and uses the match statistics, spatial factors like start and end position of the ball, and tactical factors like events, subevents for classifying whether the shot converts into a goal or not (shot conversion). As football is a low scoring game, the emphasis is scoring the winning goal, and due to the unpredictable nature of this game, there are various factors that the clubs’ coach must consider, such as team-formation, weather conditions, lineup, previous results, and many more. However, identifying the critical factors that have a substantial influence on the player’s position for the shot conversion is a challenging task. Hence, in this paper, we focus our research on forwards or strikers to predict the influential factors on shot conversion. Shotifier pipeline is a hybrid parallelism based pipeline that follows a two-step approach: First, applying Kernel Density Estimator to visually represent high concentrated zones on the football pitch for specific events in the match, along with identification of maximum ball activities based on observed ball’s start and end position. Second, using the historical match statistical data to identify the factors that have a strong influence on shot conversion by applying Support Vector Machine, XGBoost, Random Forest, and Multi-layer perceptron models. Finally, we use k-fold cross-validation to evaluate the effectiveness of the models and observe that the precision of XGBoost classifier is 70.39%, which is better than the other models considered in this study.

Shotifier: A Binary Shot Conversion Classifier Pipeline for Football Forwards

We are proposing an Open Source ROS vision pipeline for the RoboCup Soccer context. It is written in Python and offers sufficient precision while running with an adequate frame rate on the hardware of kid-sized humanoid robots to allow a fluent course of the game. Fully Convolutional Neural Networks (FCNNs) are used to detect balls while conventional methods are applied to detect robots, obstacles, goalposts, the field boundary, and field markings. The system is evaluated using an integrated evaluator and debug framework. Due to the usage of standardized ROS messages, it can be easily integrated into other teams’ code bases.

An Open Source Vision Pipeline Approach for RoboCup Humanoid Soccer

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An Open Source Vision Pipeline Approach for RoboCup Humanoid Soccer