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Titlebook: RoboCup 2018: Robot World Cup XXII; Dirk Holz,Katie Genter,Oskar von Stryk Conference proceedings 2019 Springer Nature Switzerland AG 2019

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發(fā)表于 2025-3-21 16:19:26 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱RoboCup 2018: Robot World Cup XXII
編輯Dirk Holz,Katie Genter,Oskar von Stryk
視頻videohttp://file.papertrans.cn/832/831014/831014.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: RoboCup 2018: Robot World Cup XXII;  Dirk Holz,Katie Genter,Oskar von Stryk Conference proceedings 2019 Springer Nature Switzerland AG 2019
描述.This book includes the post-conference proceedings of the 22nd RoboCup International Symposium, held in Montreal, QC, Canada, in June 2018. .The 32 full revised papers and 11 papers from the winning teams presented were carefully reviewed and selected from 51 submissions. .This book highlights the approaches of champion teams from the competitions and documents the proceedings of the 22nd annual RoboCup International Symposium. Due to the complex research challenges set by the RoboCup initiative, the RoboCup International Symposium offers a unique perspective for exploring scientific and engineering principles underlying advanced robotic and AI systems..
出版日期Conference proceedings 2019
關(guān)鍵詞agents; artificial intelligence; competition; computer vision; hardware; Human-Computer Interaction (HCI)
版次1
doihttps://doi.org/10.1007/978-3-030-27544-0
isbn_softcover978-3-030-27543-3
isbn_ebook978-3-030-27544-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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發(fā)表于 2025-3-21 22:53:20 | 只看該作者
Multi-Robot Fast-Paced Coordination with Leader Electioncan efficiently achieve their pre-defined global objective. From a wide range of multi-agent coordination sub-topics, one of the current open issues is task assignment and role selection in fast-paced environments. In homogeneous teams, where robots have the ability to dynamically change roles, work
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Visual Mesh: Real-Time Object Detection Using Constant Sample Densityansformation called Visual Mesh. It uses object geometry to create a graph in vision space, reducing computational complexity by normalizing the pixel and feature density of objects. The experiments compare the Visual Mesh with several other fast convolutional neural networks. The results demonstrat
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Learning Skills for Small Size League RoboCuphitecture. STP divides the robot behavior into a hand-coded hierarchy of plays, which coordinate multiple robots, tactics, which encode high level behavior of individual robots, and skills, which encode low-level control of pieces of a tactic. The CMDragons successfully used an STP architecture to w
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發(fā)表于 2025-3-23 00:43:45 | 只看該作者
Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL platforms is challenging because of CNNs’ excessive computational requirements. We present an end-to-end neural network solution to scene understanding for robot soccer. We compose two key neural networks: one to perform semantic segmentation on an image, and another to propagate class labels betwe
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Training a RoboCup Striker Agent via Transferred Reinforcement Learningtion spaces with continuous parameters. Advancements such as the Deep-Q Network and the Deep Deterministic Policy Gradient were a critical step in making reinforcement learning a feasible option for training agents in real world scenarios. The viability of these technologies has previously been demo
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發(fā)表于 2025-3-23 09:26:48 | 只看該作者
Playing Soccer Without Colors in the SPL: A Convolutional Neural Network Approachstem are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detector, and robot’s orientation detector obtain the highest reported detection rates. The proposed vision
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