Vision in robot soccer: a review

Nadarajah, Sivadev; Sundaraj, Kenneth

doi:10.1007/s10462-013-9401-3

Cited by 13 publications

(5 citation statements)

References 48 publications

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“…Pengindraan pada robot menjadi salah satu topik penelitian yang populer. Teknologi computer vision merupakan teknologi pengindraan pada robot, digunakan dalam berbagai kompetisi diantaranya RoboCup Soccer [5], RoboCup Rescue [6], RoboCup @ Home [7], dan RoboCup Industrial [8]. Robot simulasi hingga robot humanoid ukuran penuh yang bersaing dalam sepak bola dikompetisikan pada robocup soccer.…”

Section: Pendahuluanunclassified

Deteksi Bola Menggunakan Kombinasi Metode Filter Warna dan Bentuk Untuk Pengindraan Robot Soccer

suheryadi

2021

politeknologi

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Robot soccer is a combination of sports, robotics technology, and multi agent system, to achieve goals. Besides requiring individual intelligence, it also requires the ability to work together between individuals. The main problem in robot soccer is detecting objects in the form of balls. These problems include varying lighting that produces shadows from the ball, there are objects similar to balls, as well as realtime detection. Ease and lightweight program implementation are also needed in detecting the ball so that it is easy to implement on embedded systems. Computer vision technology helps robot soccer in detecting balls.Therefore, an alternative process is proposed to detect the ball using a combination of color filters and the shape or contour of the object. The results obtained from the experiments showed relatively effective in object detection, with a recall value of 90,69%.

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Section: Pendahuluanunclassified

Deteksi Bola Menggunakan Kombinasi Metode Filter Warna dan Bentuk Untuk Pengindraan Robot Soccer

suheryadi

2021

politeknologi

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“…In robotic soccer, situations change and diversify so dynamically that an SC method with an SVM is proposed to assess the robot's ability to control the ball [27]. In general, an SVM constructs a hyper plane that is used for classification, regression, or other tasks [28].…”

Section: A a Situation Classification Based On Isvmmentioning

confidence: 99%

An Adaptive Strategy Selection Method With Reinforcement Learning for Robotic Soccer Games

Shi¹,

Lin²,

Hwang³

et al. 2018

IEEE Access

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Robotic soccer games, which have become popular, require timely and precise decisionmaking in a dynamic environment. To address the problems of complexity in a critical situation, policy improvement in robotic soccer games must occur. This paper proposes an adaptive decisionmaking method that uses reinforcement learning (RL), and the decision-making system for a robotic soccer game is composed of two subsystems. The first subsystem in the architecture for the proposed method criticizes the situation, and the second subsystem implements decision-making policy. Inspired by the support vector machine (SVM), a situation classification method, which is called an improved SVM, embeds a decision tree structure and simultaneously addresses the problems of a large scale and multiple classifications. When a variety of situations that are collected in the field are classified and congregated into the tree structure, the problem of local strategy selection for each individual class of situations over time is regarded as a RL problem and is solved using a Q-learning method. The results of simulations and experiments demonstrate that the proposed method allows satisfactory decision-making.

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“…Similar scenes repeatedly appear in successive frames in a UAV video because of a high number of frame per seconds (fps) (at least 24 fps) [10,11]. Thus, key frame selection is required to reduce computational time for stitching images among nearly same visual information.…”

Section: System Description 21 Frame Selectionmentioning

confidence: 99%

Vision-Based 2D Map Generation for Monitoring Construction Sites Using UAV videos

Bang¹,

Kim²,

Kim³

2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-This paper presents a system to generate a 2D map using unmanned aerial vehicles (UAV) videos. The system can process the collected images for lens distortion, unstable flight, and image redundancy. The system is demonstrated using an UAV video captured on road construction site in Namyangju city, Korea. The experimental result is a high-resolution 2D map of which resolution is 6010 by 9465. This study is expected to help site managers understand site conditions and make a decision timely in diverse construction sites.

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Vision in robot soccer: a review

Cited by 13 publications

References 48 publications

Deteksi Bola Menggunakan Kombinasi Metode Filter Warna dan Bentuk Untuk Pengindraan Robot Soccer

Deteksi Bola Menggunakan Kombinasi Metode Filter Warna dan Bentuk Untuk Pengindraan Robot Soccer

An Adaptive Strategy Selection Method With Reinforcement Learning for Robotic Soccer Games

Vision-Based 2D Map Generation for Monitoring Construction Sites Using UAV videos

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