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Journal articles on the topic 'RoboCup soccer simulation 2D'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

AKIYAMA, Hidehisa. "RoboCup Soccer 2D Simulation League：Introduction to the RoboCup Soccer Simulator." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 23, no. 5 (2011): 714–20. http://dx.doi.org/10.3156/jsoft.23.5_714.

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2

AKIYAMA, Hidehisa. "RoboCup Soccer 2D Simulation League：Team Development using Sample Agen." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 23, no. 6 (2011): 838–44. http://dx.doi.org/10.3156/jsoft.23.6_838.

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3

Henn, Thomas, Jordan Henrio, and Tomoharu Nakashima. "Optimizing player’s formations for corner-kick situations in RoboCup soccer 2D simulation." Artificial Life and Robotics 22, no. 3 (2017): 296–300. http://dx.doi.org/10.1007/s10015-017-0364-3.

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4

Fukushima, Takuya, Tomoharu Nakashima, and Hidehisa Akiyama. "Evaluation-function modeling with multi-layered perceptron for RoboCup soccer 2D simulation." Artificial Life and Robotics 25, no. 3 (2020): 440–45. http://dx.doi.org/10.1007/s10015-020-00602-w.

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5

Yu, Jun, and Zhi Yi Hu. "Design and Simulation of Soccer Robots and Field Scenarios." Applied Mechanics and Materials 220-223 (November 2012): 1162–65. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1162.

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RoboCup is an international joint project to promote artificial intelligence, robotics and related fields. This paper presents a program that can draw and edit a drawing about soccer Robots and field scenarios. The program can load and save an image file, and draw 2D primitives in a painting area. These primitives can be set with different heights and tested by their compatibility with a new primitive. It also can undo/redo a drawing operation. There is a grid function to simplify the positioning of primitives. All the shapes in the drawing can be aligned appropriately. The result shows that t
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6

TANIGAWA, Shunsaku, Harukazu IGARASHI, and Seiji ISHIHARA. "1P1-R04 A Learning Algorithm of State Evaluation Functions in RoboCup Soccer Simulation League 2D(RoboCup and Robot Contest)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _1P1—R04_1—_1P1—R04_4. http://dx.doi.org/10.1299/jsmermd.2014._1p1-r04_1.

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7

Takeda, Tsutomu, Hirotaka Ono, and Masafumi Yamashita. "Introduction of RoboCup Soccer Simulation League." Proceedings of Conference of Kyushu Branch 2003.56 (2003): 305–6. http://dx.doi.org/10.1299/jsmekyushu.2003.56.305.

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8

AKIYAMA, Hidehisa, and Tomoharu NAKASHIMA. "RoboCup Soccer Simulation League : World Cup 2011." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 24, no. 1 (2012): 14–18. http://dx.doi.org/10.3156/jsoft.24.1_14.

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9

NAKASHIMA, Tomoharu, and Yosuke NAKAMURA. "RoboCup Soccer 3D Simulation League : Structure and System Installation." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 23, no. 3 (2011): 312–19. http://dx.doi.org/10.3156/jsoft.23.3_312.

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10

MENEGATTI, EMANUELE, GIOVANNI SILVESTRI, ENRICO PAGELLO, et al. "3D MODELS OF HUMANOID SOCCER ROBOT IN USARSim AND ROBOTICS STUDIO SIMULATORS." International Journal of Humanoid Robotics 05, no. 03 (2008): 523–46. http://dx.doi.org/10.1142/s0219843608001492.

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This paper describes our experience in the simulation of humanoid soccer robots using two general purposes 3D simulators, namely USARSim and Microsoft Robotics Studio. We address the problem of the simulation of a soccer match among two teams of small humanoid robots in the RoboCup Soccer Kid-Size Humanoid competitions. The paper reports the implementation of the virtual models of the Robovie-M humanoid robot platform in the two simulators. Robovie-M was the robot used by our team "Artisti" in the RoboCup 2006 competitions. This paper focuses on the procedures needed to implement the virtual m
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11

NAKASHIMA, Tomoharu, Yosuke NAKAMURA, and Takeshi SUMITANI. "RoboCup Soccer 3D Simulation League : Overview of Competitions and Agent Development." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 23, no. 4 (2011): 612–19. http://dx.doi.org/10.3156/jsoft.23.4_612.

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12

AKIYAMA, Hidehisa, Tomoharu NAKASHIMA, and Harukazu IGARASHI. "Representation and Learning Methods for Situation Evaluation in RoboCup Soccer Simulation." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 32, no. 2 (2020): 691–703. http://dx.doi.org/10.3156/jsoft.32.2_691.

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13

Wang, Jian Rong, Yang Yu, Jian Guo Wei, and Mei Yu. "A Robot Simulation System for Self-Location by Using Field Visual Information." Advanced Materials Research 748 (August 2013): 690–94. http://dx.doi.org/10.4028/www.scientific.net/amr.748.690.

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This Paper will introduce methods used to resolve self-location problem of humanoid robots. There are two method that will be mentioned in this paper. A traditional method which uses marks and flags on the soccer field for robot location will be introduced at first. This paper will also discuss how to solve the singularity problem in traditional self-location method. The optimization self-location method which uses sidelines on the soccer field will be introduced at the same time. This paper will introduce how to mapping the field sidelines in vision to the field sidelines of soccer field. Thi
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14

Zhao, Di, Yu Hong Du, Bao Lin Wu, Chen Wu, and Meng Li. "Study and Discussion on Related Technologies of RoboCup Robots." Applied Mechanics and Materials 598 (July 2014): 627–30. http://dx.doi.org/10.4028/www.scientific.net/amm.598.627.

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As a good experimental platform for multi-agent system, Robot Soccer World Cup (RoboCup) has become a hot topic of artificial intelligence. This paper reviewed the technical characteristics of related robots in Simulation, Small Size, Middle Size and Humanoid It introduced the intelligent control algorithms such as Agent structure, Kalman filtering, self-positioning of robots and ZMP stability judgment.
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15

KOBAYASHI, Yusuke, Hidenori KAWAMURA, and Keiji SUZUKI. "1A1-M05 Detection of possession type to RoboCup Soccer Simulation(Evolution and Learning for Robotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _1A1—M05_1—_1A1—M05_4. http://dx.doi.org/10.1299/jsmermd.2011._1a1-m05_1.

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16

Shahbazi, Hamed, Kamal Jamshidi, and Amir Hasan Monadjemi. "Curvilinear Bipedal Walk Learning in Nao Humanoid Robot Using a CPG Based Policy Gradient Method." Applied Mechanics and Materials 110-116 (October 2011): 5161–66. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5161.

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In this paper we will introduce a new learning approach for curvilinear bipedal walking of Nao humanoid robot using policy gradient method. A policy of walking is modeled by some policy parameters controlling some factors in programmable central pattern generators. A “Programmable” central pattern generator is made with coupled nonlinear oscillators capable to shape their state equations with some training trajectories. The proposed model has many benefits including smooth walking patterns, and modulation during walking to increase or decrease its speed. A suitable curvilinear walk was achieve
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17

Fujii, Hikari, Nobuyuki Kurihara, and Kazuo Yoshida. "Intelligent Control of Autonomous Soccer Robots Compensating for Missing Information." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (2005): 268–76. http://dx.doi.org/10.20965/jaciii.2005.p0268.

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For autonomous mobile robots, visual information is used to recognize the environment. Although the acquisition of visual information is often disturbed in the real environment, it is necessary for a robot to act appropriately even if information is missing. We compensate for missing information for autonomous mobile robots by using short-term memory (STM) to make robots act appropriately. This method involves short-term memory and action selectors. Short-term memory is constructed based on the model of human memory and the forgetting curve used in cognitive science. These action selectors use
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18

Chen, Shengbing, Gang Lv, and Xiaofeng Wang. "Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization." International Journal of Advanced Robotic Systems 13, no. 1 (2016): 25. http://dx.doi.org/10.5772/62167.

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19

Cliff, Oliver M., Joseph T. Lizier, X. Rosalind Wang, Peter Wang, Oliver Obst, and Mikhail Prokopenko. "Quantifying Long-Range Interactions and Coherent Structure in Multi-Agent Dynamics." Artificial Life 23, no. 1 (2017): 34–57. http://dx.doi.org/10.1162/artl_a_00221.

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We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team
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20

Khosravi, Abbas, Mohammad Kazem Farhadipour, Abdolreza Moghimi, and Najme Roozmand. "RoboGenius Team Description Paper Review and Develop a War Simulation Agent from AI Vision." Advanced Materials Research 403-408 (November 2011): 3555–58. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3555.

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This article contains explanations on how to develop an intelligent agent in war simulation environment by RoboGenius team. The main Focus of the team is on artificial intelligence application in war simulation server [1]. For this purpose we used a combined base code part of which is related to Robotoos team and its general parts are related to UVA2003 in 2D soccer simulation [2]. In this article we analyze the environment from an intelligent agent view point rather than exploring software engineering issues. And, considering the kind of simulation environment we study exploring issues in thi
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21

Nguyen, Quang Dang, and Mikhail Prokopenko. "Structure-Preserving Imitation Learning With Delayed Reward: An Evaluation Within the RoboCup Soccer 2D Simulation Environment." Frontiers in Robotics and AI 7 (September 16, 2020). http://dx.doi.org/10.3389/frobt.2020.00123.

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22

Riedmiller, Martin, and Daniel Withopf. "Effective Methods for Reinforcement Learning in Large Multi-Agent Domains (Leistungsfähige Verfahren für das Reinforcement Lernen in komplexen Multi-Agenten-Umgebungen)." it - Information Technology 47, no. 5 (2005). http://dx.doi.org/10.1524/itit.2005.47.5_2005.241.

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SummaryRobotic soccer requires the ability of individually acting agents to cooperate. The simulation league of RoboCup therefore offers an ideal testbed for evaluating multi-agent methods. In this paper we discuss how Reinforcement Learning (RL) methods can be succesfully applied within the scenario of learning to cooperatively score a goal. Due to the complexity of the task, enhanced methods of learning have to be applied. We discuss several approaches from literature and also present an own approach. All approaches are evaluated on a discretized version of robotic soccer, which we call grid
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23

ZHANG, FENG, YUQING ZHANG, YONGQI HU, ZEKAI CHENG, and FENG QIN. "Research on RoboCup Simulation 2D Shooting Strategy Based on Digital Twin Framework." DEStech Transactions on Social Science, Education and Human Science, mess (January 28, 2020). http://dx.doi.org/10.12783/dtssehs/mess2019/33734.

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24

Rezaeipanah, Amin, Parvin Amiri, and Shahram Jafari. "Performing the Kick During Walking for RoboCup 3D Soccer Simulation League Using Reinforcement Learning Algorithm." International Journal of Social Robotics, November 1, 2020. http://dx.doi.org/10.1007/s12369-020-00712-2.

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25

Simões, Marco A. C., Jadson Nobre, Gabriel Sousa, et al. "Generating a dataset for learning setplays from demonstration." SN Applied Sciences 3, no. 6 (2021). http://dx.doi.org/10.1007/s42452-021-04571-y.

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Abstract Coordination is an important requirement for most Multiagent Systems. A setplay is a particular instance of a coordinated plan for multi-robot systems in collective sports. Setplays are usually designed by robotics specialists using some existing tools, like the SPlanner, or by hand-coding. This work presents recent improvements to the Strategy Planner (SPlanner) and its corresponding FCPortugal Setplays Framework (FSF) to provide sophisticated setplays. This toolkit is useful to design strategic plans for robotic soccer teams as a particular case of Multi-Agent Systems (MASs). The ne
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