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On the construction of a RoboCup small size league team

机译:关于RoboCup小型联赛球队的建设

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The Robot Soccer domain has become an important artificial intelligence test bench and a widely studied research area. It is a domain with real, dynamic, and uncertain environment, where teams of robots cooperate and face adversarial competition. To build a RoboCup Small Size League (SSL) team able to compete in the world championship requires multidisciplinary research in fields like robotic hardware development, machine learning, multi-robot systems, computer vision, control theory, and mechanics, among others. This paper intends to provide insights about the aspects involved on the development of the RoboFEI RoboCup SSL robot soccer team and to present the contributions produced over its course. Among these contributions, a computer vision system employing an artificial neural network (ANN) to recognize colors, a heuristic algorithm to recognize partially detected objects, an implementation of the known rapidly-exploring random trees (RRT) path planning algorithm with additional rules, enabling the angle of approach of the robot to be controlled, and a layered strategy software system. Experimental results on real robots demonstrate the high performance of the vision system and the efficiency of the RRT algorithm implementation. Some strategy functions are also experimented, with empirical results showing their effectiveness.
机译:机器人足球领域已成为重要的人工智能测试平台和广泛研究的领域。这是一个具有真实,动态和不确定环境的领域,机器人团队在其中进行合作并面对对抗竞争。要组建一支能够参加世界锦标赛的小型机器人联赛(SSL)团队,需要在机器人硬件开发,机器学习,多机器人系统,计算机视觉,控制理论和力学等领域进行多学科研究。本文旨在提供有关RoboFEI RoboCup SSL机器人足球队发展方面的见解,并介绍其在此过程中所做出的贡献。在这些贡献中,计算机视觉系统采用人工神经网络(ANN)识别颜色,启发式算法识别部分检测到的物体,已知的快速探索随机树(RRT)路径规划算法以及其他规则的实现,从而实现了要控制的机器人的接近角度以及分层策略软件系统。在真实机器人上的实验结果证明了视觉系统的高性能以及RRT算法实施的效率。还对一些策略功能进行了实验,经验结果表明了它们的有效性。

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