一种类人机器人控制系统的设计

类人机器人是多项高技术的集成,代表机器人的尖端技术,而机器人足球比赛是其技术的具体应用.针对RoboCup足球比赛,介绍一种机器人的上层决策系统的实现方法,硬件结构使用DSP和ARM组合,在此基础上使用有限状态机理论对单个机器人的决策进攻策略进行了详细研究.真实比赛环境中的实验结果验证了该实现方法的有效性,对多智能体协作的自主决策系统的研究也具有重要意义.     

基于RBF网络的信息融合在机器人足球中的应用

机器人足球系统是综合性的人工智能研究平台。决策在机器人足球比赛中起着至关重要的作用。通过对机器人足球系统的分析,论证了信息融合应用于机器人足球系统的可行性。针对机器人足球比赛决策中的实际问题,提出了基于径向基函数(RBF)神经网络的信息融合方法,并设计了足球机器人射门实验。实验结果证明该方法有助于提高整个系统决策的准确性。     

RoboCup中型组足球机器人系统关键技术

RoboCup中型组机器人足球比赛是一个研究多自主移动机器人、机器人视觉及其他相关领域的标准测试平台。从机器人机械结构、硬件结构、体系结构、环境感知、行为及策略等方面详细介绍了RoboCup中型组足球机器人系统相关关键技术的研究现状,最后展望了RoboCup中型组足球机器人系统的技术发展趋势。     

半自主类人型机器人足球系统的优化设计

目前类人型足球机器人方面的研究还比较少,FIRA也把类人型机器人引入到足球领域不久,各个方面还不成熟,还有很大的发展空间。在这样的背景下,自主设计一个较好的类人型足球机器人系统,并且在实验和比赛中都证明了它的有效性。本文重点以视觉系统和决策系统为研究对象,针对视觉误差提出了一种新的校正方法,可以使误差率很小。针对决策系统,通过常见的决策程序设计框架的研究,提出了一个改进的程序设计框架,并且通过实验和比赛的检验,证明了该改进的程序设计框架的有效性,大大增强了机器人的比赛能力。     

基于人工生命行为选择的智能体决策的研究

人工生命行为选择是人工生命研究领域的重要问题之一,智能体作为人工生命体的一种形式,决策系统相当于是智能体的“大脑”。文章以足球机器人作为智能体的研究原型,分析了机器人足球决策系统的现状,根据仿人智能控制思想和人工生命行为选择模型,建立了基于人工生命的智能体决策系统,并借助机器人足球比赛这样一个标准任务平台,投入实际比赛中,证明结果是可行的和有效的。     

足球机器人仿真系统的研究与开发

机器人足球比赛是国际上迅速兴起的高技术对抗活动。该文介绍了利用面向对象的可视化编程技术设计足球机器人仿真系统的方法。该仿真系统包括对现实世界的仿真、决策系统设计和动画设计,能够生动地再现机器人足球比赛的激烈场面。     

机器人足球视觉图像快速识别方法的研究

机器人足球比赛作为多智能体系统（Multi-agent System, MAS）的理想研究平台已成为复杂人工智能研究的一个新兴领域.机器人足球比赛是使用计算机视觉闭环的反馈控制系统.机器人足球队员的色标设计是直接关系到系统辨识精度、实时性和抗干扰性的一个重要因素.该文通过对4种典型色标设计进行比较和分析,研究出一种比较合理的全新的队员识别算法设计思路,并针对性地提出了一种快速准确的神经网络边缘检测算法,利用足球机器人外形特征取得了良好的辨识结果.该算法对基于视觉的分布式控制式足球机器人系统的研究与开发进行了有益的探索.     

青少年机器人足球比赛系统决策生成器的研究

概述了青少年机器人足球比赛的现状,指出了程序编制是制约青少年机器人足球比赛发展的重要问题.描述了青少年机器人足球比赛系统中的决策子系统的程序结构,分析了Mirosot 3Vs3青少年机器人足球比赛的特点.建立了决策模型和产生式推理模型,设定了决策生成器的功能,并基于栅格法实现了决策生成器的开发,给青少年提供了一个编制决策的可视化工具,解决了程序编写这一难题,有力地促进青少年机器人足球比赛的发展.     

足球机器人系统中角色分配策略的设计

介绍了足球机器人系统以及决策子系统结构,该结构分为数据信息预处理,角色分配,动作安排.轮速输出。详细说明了基于机器人足球比赛的多智能体协作角色分配问题,提出了一种优化综合指标进行角色分配的方法,并在实际系统中得到验证。     

中型组足球机器人挑球系统的设计

中型组机器人足球比赛系统是进行多智能体研究的理想实验平台。中型组足球机器人有自己的“头脑”和“手脚”,后者就是它的挑球系统。设计了基于电磁原理的中型组足球机器人挑球系统,简化了系统设计,提高了系统性能,满足中型组足球机器人的控制要求。     

模糊Q学习的足球机器人双层协作模型

针对传统的足球机器人3层决策模型存在决策不连贯的问题和缺乏适应性与学习能力的缺点,提出了一种基于模糊Q学习的足球机器人双层协作模型.该模型使协调决策和机器人运动成为2个功能独立的层次,使群体意图到个体行为的过度变为一个直接的过程,并在协调层通过采用Q学习算法在线学习不同状态下的最优策略,增强了决策系统的适应性和学习能力.在Q学习中通过把状态繁多的系统状态映射为为数不多的模糊状态,大大减少了状态空间的大小,避免了传统Q学习在状态空间和动作空间较大的情况下收敛速度慢,甚至不能收敛的缺点,提高了Q学习算法的收敛速度.最后,通过在足球机器人SimuroSot仿真比赛平台上进行实验,验证了双层协作模型的有效性.     

霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

A Real-Time Hybrid Architecture for Biped Humanoids with Active Vision Mechanisms

A real-time hybrid control architecture for biped humanoid robots is proposed. The architecture is modular and hierarchical. The main robot’s functionalities are organized in four parallel modules: perception, actuation, world-modeling, and hybrid control. Hybrid control is divided in three behavior-based hierarchical layers: the planning layer, the deliberative layer, and the reactive layer, which work in parallel and have very different response speeds and planning capabilities. The architecture allows: (1) the coordination of multiple robots and the execution of group behaviors without disturbing the robot’s reactivity and responsivity, which is very relevant for biped humanoid robots whose gait control requires real-time processing. (2) The straightforward management of the robot’s resources using resource multiplexers. (3) The integration of active vision mechanisms in the reactive layer under control of behavior-dependant value functions from the deliberative layer. This adds flexibility in the implementation of complex functionalities, such as the ones required for playing soccer in robot teams. The architecture is validated using simulated and real Nao humanoid robots. Passive and active behaviors are tested in simulated and real robot soccer setups. In addition, the ability to execute group behaviors in real- time is tested in international robot soccer competitions.     

不同个性的情感机器人表情研究

在情感机器人研究中,不同个性的面部表情是情感机器人增强真实感的重要基础。为实现情感机器人更加丰富细腻的表情,将人类的个性特征引入情感机器人,分析个性理论和情感模型理论,得知不同个性机器人的情感强度。结合面部动作编码系统中面部表情与机器人控制点之间的映射关系,得到情感机器人不同个性的基本表情实现方法。利用Solidworks建立情感机器人脸部模型,在ANSYS工程软件中将SHFR-Ⅲ情感机器人脸部模型设置为弹性体,通过有限元仿真计算方法,对表情的有限元仿真方法进行了探究,得到实现SHFR-Ⅲ不同个性基本表情的控制区域载荷大小和仿真结果。最后,根据仿真结果,进行SHFR-Ⅲ情感机器人不同个性的表情动作实验。实验结果表明,有限元表情仿真可以指导SHFR-Ⅲ情感机器人实现近似人类的不同个性的基本面部表情。     

Policy gradient learning for a humanoid soccer robot

In humanoid robotic soccer, many factors, both at low-level (e.g., vision and motion control) and at high-level (e.g., behaviors and game strategies), determine the quality of the robot performance. In particular, the speed of individual robots, the precision of the trajectory, and the stability of the walking gaits, have a high impact on the success of a team. Consequently, humanoid soccer robots require fine tuning, especially for the basic behaviors. In recent years, machine learning techniques have been used to find optimal parameter sets for various humanoid robot behaviors. However, a drawback of learning techniques is time consumption: a practical learning method for robotic applications must be effective with a small amount of data. In this article, we compare two learning methods for humanoid walking gaits based on the Policy Gradient algorithm. We demonstrate that an extension of the classic Policy Gradient algorithm that takes into account parameter relevance allows for better solutions when only a few experiments are available. The results of our experimental work show the effectiveness of the policy gradient learning method, as well as its higher convergence rate, when the relevance of parameters is taken into account during learning.     

Self-modeling in humanoid soccer robots

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.     

Intelligent decision making in multi-agent robot soccer system through compounded artificial neural networks

This paper proposes a two-stage approach using artificial neural networks for the intelligent decision-making by the robots in a MiroSot small league. The first stage involves the use of an evolutionary algorithm for getting a rough estimate of the neural network weight matrices. The proposed approach is then generalized to the case of quick, intelligent and accurate decision-making in the case of a robot soccer system with robots utilizing the concept of compounded artificial neural networks. In the proposed approach a soccer field is divided into three zones so that the decision of the robots depends on the zone of the ball at any instant. The concept of a forward robot is also introduced in this paper to enhance the accuracy of the decision-making with the global strategy of advancing towards the goal area of the opponent for scoring a goal. Simulation results indicate that the proposed techniques are very effective in taking intelligent decision-making in a multi-agent robot soccer system in MiroSot small league as well as middle league.     

Fuzzy logic decision making for multi-robot security systems

The paper proposes a fuzzy logic decision making system for security robots that deals with multiple tasks with dynamically changing scene. The tasks consist of patrolling the environment, inspecting for missing items, chasing and disabling intruders, and guarding the area. The decision making considers robot limitations such as maximum floor coverage per robot and remaining robot battery energy, as well as cooperation among robots to complete the mission. Each robot agent makes its own decision based on its internal information as well as information broadcast to it by other robots about events such as intruder sighting. As a result the multi-robot security system is distributive without a central coordinator. The system has been implemented both in simulations and on actual robots and its performance has been verified under different scenarios.     

基于赛场态势的群体智能决策技术

复杂环境下态势的正确评估是开发高水平决策系统所必需解决的关键问题。为了对机器人足球赛场态势进行精确的评估,结合专家经验和对实际比赛的分析,提炼影响赛场态势的几个关键因素,在此基础上提出一个全新的态势决策模型。该模型中机器人集合构成一个决策群体,智能群体决策的结果使得足球机器人系统具有很强的战斗力,其有效性已在实验及比赛中得到验证。     

足球机器人对彩球的鲁棒跟踪识别

在基于视觉的足球机器人系统中,对场上焦点目标——球的动态跟踪识别是系统设计的第一要务。针对半自主微型机器人足球比赛中的小球易受场上干扰、小车遮挡造成的识别丢失问题,提出基于预测与搜索窗的图像目标跟踪识别方法。通过最小二乘法预测丢失小球的可能位置,将图像目标搜索限制在局部小区域内,并利用搜索窗内的在线状态信息加以判断,实现运动目标被遮挡情况下的有效跟踪识别。实验与比赛结果统计表明,该方法实时跟踪识别效果好、鲁棒性强。