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首页> 外文期刊>BRAIN. Broad Research in Artificial Intelligence and Neurosciences >Modeling and Implementation of Omnidirectional Soccer Robot with Wide Vision Scope Applied in Robocup-MSL
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Modeling and Implementation of Omnidirectional Soccer Robot with Wide Vision Scope Applied in Robocup-MSL

机译:Robocup-MSL中的宽视野全方位足球机器人的建模与实现

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The purpose of this paper is to design and implement a middle size soccer robot to conform RoboCup MSL league. First, according to the rules of RoboCup, we design the middle size soccer robot, The proposed autonomous soccer robot consists of the mechanical platform, motion control module, omni-directional vision module, front vision module, image processing and recognition module, investigated target object positioning and real coordinate reconstruction, robot path planning, competition strategies, and obstacle avoidance. And this soccer robot equips the laptop computer system and interface circuits to make decisions. In fact, the omnidirectional vision sensor of the vision system deals with the image processing and positioning for obstacle avoidance andtarget tracking. The boundary-following algorithm (BFA) is applied to find the important features of the field. We utilize the sensor data fusion method in the control system parameters, self localization and world modeling. A vision-based self-localization and the conventional odometrysystems are fused for robust selflocalization. The localization algorithm includes filtering, sharing and integration of the data for different types of objects recognized in the environment. In the control strategies, we present three state modes, which include the Attack Strategy, Defense Strategy and Intercept Strategy. The methods have been tested in the many Robocup competition field middle size robots.
机译:本文的目的是设计和实现一个符合RoboCup MSL联赛的中型足球机器人。首先,根据RoboCup的规则,设计了一款中型足球机器人,拟议的自主足球机器人由机械平台,运动控制模块,全向视觉模块,前视模块,图像处理和识别模块,被调查对象组成目标定位和真实坐标重建,机器人路径规划,竞争策略以及避障。这款足球机器人配备了便携式计算机系统和接口电路来做出决定。实际上,视觉系统的全向视觉传感器处理图像处理和定位,以避开障碍物和进行目标跟踪。边界跟踪算法(BFA)用于查找该领域的重要特征。我们在控制系统参数,自定位和世界建模中利用传感器数据融合方法。基于视觉的自我定位和常规测距系统融合在一起,实现了强大的自我定位。本地化算法包括对环境中识别的不同类型对象的数据进行过滤,共享和集成。在控制策略中,我们提出了三种状态模式,包括攻击策略,防御策略和拦截策略。该方法已在许多Robocup竞争领域的中型机器人中进行了测试。

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