基于ARM的类人足球机器人视觉识别系统的设计

针对类人足球机器人在复杂赛场环境中对目标色块识别能力的不足,设计一种基于ARM的类人足球机器人视觉识别系统。软硬件设计上,以搭载Linux系统的三星S3C6410作为主控图像处理芯片,用飞思卡尔MC9S12XS128单片机作为舵机控制器,以罗技C270摄像头作为视觉传感器。分割识别算法上,采用YUV颜色空间下的阈值法与区域生长的混合分割算法对色块信息进行快速分割和识别。实验表明,该设计可准确识别出较复杂环境中的目标色块并定位出中心坐标。     

全自主足球机器人混合视觉系统的设计与实现

根据中型组机器人足球比赛的需要,设计并实现了一套由全向视觉、前向单目视觉和嵌入式视觉构成 的混合视觉系统．全向视觉具有360± 的水平视角,信息量大、实时性好,但近端信息的感知识别精度较低,前向单 目视觉和嵌入式视觉就是为了解决该问题而加入的．前向单目视觉主要用于识别位于机器人正面不远处的目标,且 可给出精确的目标位置信息,同时全向视觉和前向单目视觉的处理都是在机器人上位机上进行的．为了解决机器人 非前向近端信息缺失的问题,在不增加上位机负担的情况下,本文通过下位机的嵌入式视觉识别机器人周边非前向 近端的动态目标或障碍物,较好地解决了机器人360± 范围内近端信息的获取问题．实验结果表明,该视觉系统较好 地实现了对机器人远端、近端目标的识别．     

基于DSP的欠驱动体操机器人的摇起控制设计

针对欠驱动机器人控制系统,给出一种基于DSP控制的类人形的三关节欠驱动体操机器人。首先以ADSP2181为核心设计出控制器,通过高速PCI总线与上位机PC通讯,采用直流电机伺服控制。然后依据建立的体操机器人动力学模型提出基于能量增加的正弦和斜坡函数输入方式,经对体操机器人作摇起控制实验,实验显示,设计的三关节欠驱动体操机器人控制系统满足实时性、稳定性和准确性要求。     

一种改进的类人足球机器人彩色目标识别算法

针对类人足球机器人视觉需求,提出一种结合区域生长和基于形状判别的阈值自适应更新的彩色目标识别算法。该算法在HSI空间基于S分量把图像分为高饱和区域和低饱和区域,在高饱和区域基于H分量采用区域生长算法识别目标;通过目标形状判别自适应更新阈值,并用新阈值更新区域生长中原来的阈值,以稳定准确地识别彩色目标。在类人足球机器人系统中的成功应用表明,该算法能在不同光照条件下稳定地识别出彩色目标,对光照环境有良好的适应性和鲁棒性,具有良好的识别效果。     

基于DSP机器视觉的矩形类物体识别研究

针对机器人控制对视觉的实时性要求,设计了一种基于TMS320C6416图像处理平台的嵌入式机器人视觉系统.本系统以VC 6.0为平台来开发控制DSP的操作界面,通过该操作界面控制DSP对矩形类物体图像的采集、处理和识别,后DSP把识别的结果传送给主机,为机器人手臂的轨迹规划提供目标物体信息.文中给出了系统的硬件设计方案和软件实现过程.     

基于AT89C52与工控机的迎宾机器人设计

迎宾机器人已成为机器人研究领域的新宠,本文介绍了一种新型迎宾机器人的设计,包括机构结构设计与控制系统的设计。机械结构设计部分主要详细说明了移动机构、机身、手臂的设计特点;控制系统的功能包括动作功能、语音识别及人脸识别。控制系统的硬件采用了工控机EAC-7001与单片机AT89C52,语音识别与人脸识别分别采用了科大飞讯语音识别软件及Lenovo VeriFace3.5人脸识别软件。另外机器人首次采用类人的双脚交叉行走的方式,具有自主运动和避障碍物的能力。经过调试,证明该机器人已满足迎宾机器人的基本要求。     

基于特征差异的彩色目标快速识别方法

本文从人类视觉系统识别彩色目标的特点出发,提出一种基于特征差异的彩色目标快速识别方法,从而有效地提高了机器人视觉系统的实时性,并进行了多物体情况下特定目标的识别与动态目标的识别实验。     

一种稳健的足球机器人目标识别算法

视觉系统是类人足球机器人获取环境信息的主要途径。在比赛中,受场地光照等比赛环境的影响,用传统的分割识别算法难以达到满意的效果。文中提出一种结合目标颜色和形状信息的识别算法。该算法在HSI空间执行基于颜色信息的快速阀值分割,获取目标像素,并且加入了自适应阀值更新,然后利用目标像素和目标形状信息执行优化边缘检测识别目标,最终获得目标在图像中准确的位置信息。实验证明：该算法能长时间在不同光照条件下稳定获取对象在图像中的位置信息,满足实时性的要求。     

基于在线识别的机器人动态手眼协调

本文研究了基于在线识别的机器人动态手眼协调,通过视觉在线识别目标的结果来 动态规划机器人的运动,改善了系统的柔性,提高了系统对环境的适应能力．实验证明,文 中所采用的识别算法具有很好的实时性和可靠性．引入语音控制改善了人机交互．     

基于SOPC的机器人伺服控制器的研究

针对传统工业机器人系统的局限性,提出了一种高实时性的机器人视觉伺服控制系统.系统利用Altera公司的Nios II软核设计一个机器人实时目标跟踪控制器.控制器由Nios II作为处理器,采用基于特征差异的目标识别方法辨识目标并采用线性卡尔曼滤波器算法来快速完成运动估计及进一步分析和校正,实现快速而准确的跟踪.从而改善了多关节机器人跟踪运动目标过程中存在的彩色图像数据量大、处理时间长的问题,提高了实时性.     

卓越Ⅰ型仿人机器人底层关节控制器的设计

为了实现卓越Ⅰ型机器人在实际环境中稳步行走,设计了底层控制系统中的关节控制器用于控制舵机。选用DSP TMS320F2812作为处理器,充分利用其全数字型,集成度高,体积小,低功耗以及可实现多轴运动的特点,对底层各个关节舵机进行控制,实现既定舵机的运转,以及将舵机的转速和位置等相关传感器的信息反馈给上级控制处理器实现对关节舵机精确的控制,并通过DSP TMS320F2812的两个事件管理器分别同时控制机器人的左右腿的关节舵机单元,提高了机器人在实际行走以及执行任务过程中的实时性、准确性以及稳定性。     

Multi-level offloading decision on efficient object tracking for humanoid robot

Energy efficiency is an important issue for a humanoid robot to track an object, especially robot using battery packs with limited capacity. The tracking algorithm is implemented by many functions, such as walking patterns, balance control, object recognition, path planning, etc. Performing these functions on a remote server can offload the computation on robot for energy efficiency. The offloading needs a proper decision rule in respect to the efficiency and performance. This paper presents a multi-level decision method by classifying the functions into three levels related to stability, real-time performance, and energy efficiency. Based on these levels, the decision rules are designed to offload these functions to track the object efficiently. They are validated by a humanoid robot platform called DARwIn-OP. This method could also be used to minimize the energy consumption on other tasks of a humanoid robot.     

A closed-loop approach for tracking a humanoid robot using particle filtering and depth data

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.     

Adequate motion simulation and collision detection for soccer playing humanoid robots

In this paper a humanoid robot simulator based on the multi-robot simulation framework (MuRoSimF) is presented. Among the unique features of this simulator is the scalability in the level of physical detail in both the robot’s motion and sensing systems. It facilitates the development of control software for humanoid robots which is demonstrated for several scenarios from the RoboCup Humanoid Robot League.Different requirements exist for a humanoid robot simulator. E.g., testing of algorithms for motion control and postural stability require high fidelity of physical motion properties whereas testing of behavior control and role distribution for a robot team requires only a moderate level of detail for real-time simulation of multiple robots. To meet such very different requirements often different simulators are used which makes it necessary to model a robot multiple times and to integrate different simulations with high-level robot control software.MuRoSimF provides the capability of exchanging the simulation algorithms used for each robot transparently, thus allowing a trade-off between computational performance and fidelity of the simulation. It is therefore possible to choose different simulation algorithms which are adequate for the needs of a given simulation experiment, for example, motion simulation of humanoid robots based on kinematical, simplified dynamics or full multi-body system dynamics algorithms. In this paper also the sensor simulation capabilities of MuRoSimF are revised. The methods for motion simulation and collision detection and handling are presented in detail including an algorithm which allows the real-time simulation of the full dynamics of a 21 DOF humanoid robot. Merits and drawbacks of the different algorithms are discussed in the light of different simulation purposes. The simulator performance is measured and illustrated in various examples, including comparison with experiments of a physical humanoid robot.     

MAHRU-M: A mobile humanoid robot platform based on a dual-network control system and coordinated task execution

This paper introduces a mobile humanoid robot platform able to execute various services for humans in their everyday environments. For service in more intelligent and varied environments, the control system of a robot must operate efficiently to ensure a coordinated robot system. We enhanced the efficiency of the control system by developing a dual-network control system. The network system consists of two communication protocols: high-speed IEEE 1394, and a highly stable Controller Area Network (CAN). A service framework is also introduced for the coordinated task execution by a humanoid robot. To execute given tasks, various sub-systems of the robot were coordinated effectively by this system. Performance assessments of the presented framework and the proposed control system are experimentally conducted. MAHRU-M, as a platform for a mobile humanoid robot, recognizes the designated object. The object’s pose is calculated by performing model-based object tracking using a particle filter with back projection-based sampling. A unique approach is used to solve the human-like arm inverse kinematics, allowing the control system to generate smooth trajectories for each joint of the humanoid robot. A mean-shift algorithm using bilateral filtering is also used for real-time and robust object tracking. The results of the experiment show that a robot can execute its services efficiently in human workspaces such as an office or a home.     

Real-time continuous ZMP pattern generation of a humanoid robot using an analytic method based on capture point

This paper introduces an analytic method to generate a continuous ZMP pattern based on a capture point (CP). When a target CP is decided in real-time, the pattern generator makes the CP, ZMP, which it is within convex hull of the supporting feet area, and CoM patterns without discontinuity by closed-form solutions for a single step. Therefore, the proposed pattern generation method does not need a ZMP pattern modification, numerical iterations, and future ZMPs. The method is employed to treat applications such as step length change while walking and push recovery during walking in place. Furthermore, since compliant characteristics such as body oscillation appear in the humanoid robot, we introduce a system model, a double inverted pendulum model with flexible joints for the model-based control. Finally, the real-time walking pattern generation method and the walking control scheme are verified by experiments with the humanoid robot HUBO2.     

仿人机器人视觉导航中的实时性运动模糊探测器设计

针对仿人机器人视觉导航系统的鲁棒性受到运动模糊制约的问题,提出一种基于运动模糊特征的实时性异常探测方法. 首先定量地分析运动模糊对视觉导航系统的负面影响,然后研究仿人机器人上图像的运动模糊规律,在此基础上对图像的运动模糊特征进行无参考的度量,随后采用无监督的异常探测技术,在探测框架下对时间序列上发生的图像运动模糊特征进行聚类分析,实时地召回数据流中的模糊异常,以增强机器人视觉导航系统对运动模糊的鲁棒性. 仿真实验和仿人机器人实验表明:针对国际公开的标准数据集和仿人机器人NAO数据集,方法具有良好的实时性（一次探测时间0.1s）和有效性（召回率98.5%,精确率90.7%）. 方法的探测框架对地面移动机器人亦具有较好的普适性和集成性,可方便地与视觉导航系统协同工作.     

Target Speech Detection and Separation for Communication with Humanoid Robots in Noisy Home Environments

People usually talk face to face when they communicate with their partner. Therefore, in robot audition, the recognition of the front talker is critical for smooth interactions. This paper presents an enhanced speech detection method for a humanoid robot that can separate and recognize speech signals originating from the front even in noisy home environments. The robot audition system consists of a new type of voice activity detection (VAD) based on the complex spectrum circle centroid (CSCC) method and a maximum signal-to-noise ratio (SNR) beamformer. This VAD based on CSCC can classify speech signals that are retrieved at the frontal region of two microphones embedded on the robot. The system works in real-time without needing training filter coefficients given in advance even in a noisy environment (SNR > 0 dB). It can cope with speech noise generated from televisions and audio devices that does not originate from the center. Experiments using a humanoid robot, SIG2, with two microphones showed that our system enhanced extracted target speech signals more than 12 dB (SNR) and the success rate of automatic speech recognition for Japanese words was increased by about 17 points.     

仿人机器人分布式控制系统设计与实现

针对BHBIP-1型双足步行机器人对控制系统的实时性和稳定性要求,设计实现具有3层结构的分布式控制系统,包括基于PC104的主控层、CAN总线通信层和执行层,给出上位机、关节位置伺服控制器的硬件及控制软件设计方案。样机调试结果表明,该控制系统性能良好,机器人行走过程稳定,抗干扰能力强。