United States

In this paper, we propose a method for predicting the path of the ball on the soccer field for the humanoid robots. A cost-function-based k-nearest neighbor regression method is first proposed to account for the part of the prediction which is based on previously observed data. Next, the autoregression method is utilized in order to carry out the prediction based on the current ball path. Finally, these two methods are combined to form the final prediction model. Moreover, two different schemes are introduced based upon the proposed model: fixed and adaptive schemes. In fixed scheme, the prediction is made once during the
initial steps of the motion and is used throughout the whole ball movement. However, in adaptive scheme, autoregression method coefficients are updated in fixed predened steps during the motion. This is beneficial to robustify the prediction against an externally applied disturbance on the ball path. Our proposed method is tested by simulation and practical implementation and the results demonstrate a high precision rate.

RoboCup 2021

Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods

k-nearest neighbor regression.

autoregression

humanoid robots

ball path prediction

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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

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You need to be registered to explore this event!

The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

Mobile robot control requires precision and accuracy to react to unpredictable situations. However, it is non-trivial to design a low-level controller for each wheel that combines fast response and stability in the presence of disturbances. The Proportional-Integral (PI) controller algorithm is the most used technique for DC motors; however, tuning these controllers can become a time-consuming task depending on the number of robots and tuning methodology. We propose a novel telemetry-based strategy to find suitable PI controller parameters more quickly using accurate motor models of the omnidirectional mobile robots obtained from an on-site data sampling mechanism. We evaluate our approach on an omnidirectional robot designed within the RoboCup Small Size League (SSL) competition rules. The results compare the proposed method with a based on Quantitative Feedback Theory (QFT) approach. Our strategy improved on average the robot's performance by 17.95% when using Integral Absolute Error (IAE) and by 12.75% when using Integral Squared Error (ISE) criteria.

A Telemetry-based PI Tunning Strategy for Low-level Control of an Omnidirectional Mobile Robot

Mobile robots and many edge AI devices have a need to trade off computational power against power consumption, battery size, and time between charges. Consequently, it is common for such devices to have significantly less computational power than the powerful GPU-based systems typically used to train and evaluate deep neural networks. Object detection is a key aspect of visual perception for robots and edge devices but popular object de-tection architectures that run fastest on GPU based systems or that are de-signed to maximize mAP with large input image sizes may not scale well to edge devices. In this work we evaluate the latency and mAP of several model architectures from the YOLO and SSD families on a range of devices repre-sentative of robot and edge device capabilities. We also evaluate the effect of runtime framework and show that some unexpected large differences can be found. Based on our evaluations we propose new variations of the YOLO-LITE architecture which we show can provide increased mAP at reduced latency.

Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices

We 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.

TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021

This article aims to present the contributions of the soccer robot project based on RoboCupJunior Soccer Open. The project proposes the 3D modeling of the robot in order to create a virtual version before the physical robot. In the virtual robot, it is implemented the algorithm, are validated the game strategies, and tested the design functionality in the robot structure through the 3D simulation environment. This virtual environment was developed within the parameters of the competition to provide greater consistency and integrity during the simulations, which allows time optimization, project improvement, problem correction and solution implementation before robot construction. The potential of the simulation environment is visible, which can encourage a 3D Simulation modality of Soccer at RoboCupJunior.

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A Telemetry-based PI Tunning Strategy for Low-level Control of an Omnidirectional Mobile Robot

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