仿生型机器人眼球运动控制系统建模

首先，分析了人眼眼球运动的特点和形式．然后，在人眼解剖学和生理学研究的基础上，根据控制眼球运动的神经回路，建立了可实现人眼功能的仿生型机器人眼三维眼球运动控制系统的数学模型．在与生理学试验相同的条件下对该模型用Matlab 65进行了仿真实验，仿真结果与生理学实验结果对比表明，该模型能自适应地实现人眼的平滑追踪、前庭动眼反射、视动反射及其复合运动．     

仿生眼的研究现状与发展趋势

首先,在分析人眼眼球运动的特点和形式的基础上,详细介绍了国内外仿生眼研究对扫视性眼球运动、平滑性眼球运动、异向眼球运动、前庭动眼反射、视动反射等的模仿,最后总结了仿生眼研究的特点和发展趋势．     

基于神经网络的机器人手眼无标定平面视觉跟踪

在手眼关系及摄像机模型完全未知的情况下,建立了眼在手上机器人平面视觉跟踪 问题的非线性视觉映射模型,将图像特征空间和机器人工作空间紧密地联系起来.在此基础 上,设计了基于人工神经网络的视觉跟踪控制方案,将视觉跟踪问题转化为图像特征空间中 的定位问题.仿真结果表明该算法能完全消除稳态跟踪误差,具有很强的环境适应性和容错 能力,算法简单,易于实时实现.     

基于视觉感知的海生物吸纳式水下机器人目标捕获控制

针对近海水产养殖环境下海生物目标的机器捕捞,设计了一款海生物吸纳式水下机器人.该水下机器人可以采取手动遥控吸取和视觉伺服控制吸取方式完成对海生物目标的机器捕捞.为了实现基于视觉感知的目标捕获控制,在摄像机平面坐标系上建立了水下机器人和目标之间的运动学关系,并在此基础上提出了自适应递归神经网络控制器.通过设计递归神经网络估计和补偿外界环境干扰,利用S面函数使水下机器人快速到达期望位置并保持稳定,结合递归神经网络和系统动力学模型设计鲁棒函数进一步提高非线性系统在视觉控制中的可靠性和稳定性.最后,在近海自然养殖条件下对海生物进行视觉跟踪控制实验,实现了对海生物目标的主动吸取控制,验证了该控制器的功能.     

空间机器人视觉伺服半物理仿真的原理与实现

针对在地面很难对空间机器人目标捕获过程进行微重力环境下的真实实验问题,建立了半物理仿真系统,以对图像处理、路径规划以及控制等关键算法进行验证,该系统的关键在于将"虚拟环境"与"真实环境"结合起来,利用真实视觉对虚拟环境进行3D重构,以计算目标的相对位置和姿态,实现对视觉伺服控制的闭环仿真.从理论上推导了半物理仿真的原理,并提出了等效投影模型的标定方法,仿真结果表明了该方法的有效性.     

基于模糊神经网络的双目视觉定位系统研究

针对移动机器人对未知不确定环境缺乏自适应性的缺点,在活动关节自由度的基础上构建了基于模糊神经网络的双目视觉定位系统.采用自组织学习和监督学习相结合的混合算法,从而避免了移动机器人在移动过程中的振动所带来的误差.此外,通过控制伺服电机实现摄像机的移动,降低了双目视觉定位系统的成本.仿真实验结果表明,该系统具有较高的精确度及响应速度.     

基于无模型自适应控制的视觉伺服

传统的机器人视觉伺服控制技术需要已知机器人精确的动力学和运动学模型以及机器人的手-眼参数。然而,由于机器人建模、手-眼标定等过程存在一定误差,因此很难精确获得视觉伺服控制模型,从而影响机器人视觉伺服系统的精度和收敛速度。针对这一难题,本文提出一种基于无模型自适应控制方法（MFAC）的机器人视觉伺服技术。利用视觉伺服系统的输入与输出数据,实现自适应视觉伺服控制,即通过MFAC在线估计机器人伺服控制器中的雅各比矩阵,并结合滑模控制器,实现机器人对目标的快速精确跟踪。实验结果表明,本文提出的方法在系统参数变化引起的未知扰动情况下仍能保证伺服控制器平稳收敛,并且能够减小视觉跟踪误差。     

基于视觉伺服的输电线巡检机器人抓线控制

巡检机器人在自动越障时，需要完成机器人手臂的准确抓线控制。结合输电线的几何特征和摄像机成像原理，提出了一种基于单摄像机的立体视觉方法来确定输电线的位置和姿态。结合该定位方法及视觉伺服理论建立机械手抓线伺服控制模型。在自行研制的巡检机器人进行了视觉伺服抓线实验。实验结果验证了该方法的有效性。     

相机姿态安装误差对单目视觉定位精度的影响

现有单目视觉定位方法由于相机姿态误差而存在定位精度不高的问题,但鲜见文献对此进行定量分析.针对该问题从定性分析和定量仿真的角度研究了相机姿态角的安装误差对单目视觉定位精度的影响.在距离为211.377 m的平坦直线道路上进行了三组实验,实验结果和仿真结果吻合较好,经相机姿态安装误差校正以后,三组实验中行驶轨迹弯曲的现象得到了纠正,并获得了最大误差0.45％的测距精度.所得结论对提高单目视觉定位精度具有指导意义.     

基于视觉感知的平面设计背景图像裁剪

在平面设计工作中，为了解决前景图像与背景画布大小的不匹配问题，设计师通常需要对背景图像进行裁剪，但已有的裁剪方法没有考虑到用户对裁剪后视觉效果的主观体验.为此，本文提出了一种基于视觉感知的平面设计背景图像裁剪方法，首先基于全卷积神经网络训练平面设计数据集，建立视觉显著性预测模型，对图像进行视觉显著性预测；然后基于眼动跟踪技术，利用获得的眼动跟踪数据来识别图像的重要区域；最后将上述两步的结果进行融合，得到建议裁剪区域.实验结果表明，该方法的图像裁剪结果比已有方法更能吸引用户的视觉注意，具有更好的主观体验，且裁剪效果在平均重叠率和边界位移误差等指标上均有一定提升，验证了该方法在具体平面设计工作中的有效性与实用性.     

Measurement and storage of a network of jacobians as a method for the visual positioning of a robot arm

The goal of this paper is to describe a method to position a robot arm at any visible point of a given workspace without an explicit on line use of the analytical form of the transformations between real space and camera coordinates (camera calibration) or between cartesian and joint coordinates (direct or inverse kinematics of the robot arm). The formulation uses a discrete network of points distributed all over the workspace in which a procedure is given to measure certain Jacobian matrices which represent a good local linear approximation to the unknown compound transformation between camera and joint coordinates. This approach is inspired by the biological observation of the vestibulo-ocular reflex in humans (VOR). We show that little space is needed to store the transformation at a given scale, as feedback on the visual coordinates is used to improve precision up to the limit of the visual system. This characteristic also allows the plant to cope with disturbances in camera positioning or robot parameters. Furthermore, if the dimension of the visual space is equal or bigger than the motor space dimension, the transformation can be inverted, resulting in a realistic model of the plant able to be used to train other methods for the determination of visuo-motor mapping. As a test of the method an experiment to position a real robot arm is presented, together with another experiment showing the robot executing a simple task (building a tower of blocks).     

Adaptive walking control of quadruped robots based on central pattern generator （CPG） and reflex

This paper presents a central pattern generator (CPG) and vestibular reflex combined control strategy for a quadruped robot. An oscillator network and a knee-to-hip mapping function are presented to realize the rhythmic motion for the quadruped robot. A two-phase parameter tuning method is designed to adjust the parameters of oscillator network. First, based on the numerical simulation, the influences of the parameters on the output signals are analyzed, then the genetic algorithm (GA) is used to evolve the phase relationships of the oscillators to realize the basic animal-like walking pattern. Moreover, the animal’s vestibular reflex mechanism is mimicked to realize the adaptive walking of the quadruped robot on a slope terrain. Coupled with the sensory feedback information, the robot can walk up and down the slope smoothly. The presented bio-inspired control method is validated through simulations and experiments with AIBO. Under the control of the presented CPG and vestibular reflex combined control method, AIBO can cope with slipping, falling down and walk on a slope successfully, which demonstrates the effectiveness of the proposed walking control method.     

Communication Interface with Eye-Gaze and Head Gesture Using Successive DP Matching and Fuzzy Inference

This paper proposes a communication interface with eye-gaze and head gesture. Visual sensorimotor integration with eye-head cooperation is considered, especially head gesture accompanied with vestibulo-ocular reflex is used for selecting object in the screen. Eye-mark recorder and motion tracking system were used to tracking eye movement and head movement, respectively. Nonverbal response animation with eye contact was introduced in the interaction system. In identifying the head gesture, we adopted a modified dynamic programming matching method and fuzzy inference.     

Realization of Dynamic Walking and Running of the Quadruped Using Neural Oscillator

In the present study we attempt to induce a quadruped robot to walk dynamically on irregular terrain and run on flat terrain by using a nervous system model. For dynamic walking on irregular terrain, we employ a control system involving a neural oscillator network, a stretch reflex and a flexor reflex. Stable dynamic walking when obstructions to swinging legs are present is made possible by the flexor reflex and the crossed extension reflex. A modification of the single driving input to the neural oscillator network makes it possible for the robot to walk up a step. For running on flat terrain, we combine a spring mechanism and the neural oscillator network. It became clear in this study that the matching of two oscillations by the spring-mass system and the neural oscillator network is important in order to keep jumping in a pronk gait. The present study also shows that entrainment between neural oscillators causes the running gait to change from pronk to bound. This finding renders running fairly easy to attain in a bound gait. It must be noticed that the flexible and robust dynamic walking on irregular terrain and the transition of the running gait are realized by the modification of a few parameters in the neural oscillator network.     

Walking up and down hill with a biologically-inspired postural reflex in a quadrupedal robot

This paper presents a control strategy, biologically-inspired postural reflex, based directly on animal behaviors, which allows a quadrupedal robot to walk up and down hill smoothly. A central pattern generator (CPG) and a hip-to-knee mapping function are employed to realize the basic rhythmic motion for the quadrupedal robot. The trunk pitch angle feedback of the robot is linearly introduced to the CPG, spontaneously changing the mid-positions of the four legs to make postural adjustments as the way a cat does. Thereby, slipping and falling-down are greatly reduced. Numerical simulations and experimental implementation on a physical quadrupedal robot demonstrate the effectiveness of the proposed approach.     

智能机器人神经心理模型

为了从结构、功能和行为3个层面对智能机器人体系结构进行一体化描述,本文提出了智能机器人神经心理模型.借鉴脑的3个基本机能联合区理论建立了智能机器人的神经生理结构模型,将机器人思维系统划分为感知区、反射区和慎思区,每个区均由三级皮层构成,采用拓展的BD I逻辑(机器人心智逻辑RML)描述机器人的认知心理机制,给出了神经心理框架下的机器人智能行为过程,从理论上证明了RML的可靠性与完备性,采用水下机器人编队穿越未知雷区的对比仿真实验验证了神经心理模型的可行性和有效性.     

沟壑类非连续地形下的四足机器人运动控制

针对四足机器人踏入野外沟攀或踏上非结构化下行台阶时出现的姿态失稳问题,提出模仿生物神经反射机理的抗垂直惯性力平衡控制方法,即通过检测躯干的俯仰角突变信息,触发姿态反射,通过四条腿协调运动快速调整机器人姿态,抵抗惯性力造成的瞬时失稳.本文利用一个8自由度的四足仿生机器人设计了对比实验:机器人可成功跨越1.2倍跨距的沟壑并...     

Motion control of a quadruped robot in unknown rough terrain using 3D spring damper leg model

This paper presents a simple method to control the motion of a quadruped robot in unknown rough terrain using a full dynamic model. First, using the stiffness control method, the behavior of the four legs is approximated using four 3D spring damper systems. In this way, the dynamic model can be derived in Cartesian space. Based on this model, a control strategy is proposed to preserve the asymptotical stability of the system. In addition, a reflex motion control is introduced to cope with the rotational disturbance of the robot body. Finally, dynamic simulations and experiments of a quadruped walking robot were performed on unknown rough terrain to verify the proposed method.     

移动机器人条件反射能力的实现

Brooks的包容体系结构中,移动机器人控制器各个行为之间的关系是固定不变 的,可以看作实现了机器人的非条件反射能力,因此控制系统没有适应性．本文提出了条件 反射能力的实现方法,该方法能够和包容体系结构紧密结合,增强移动机器人的适应能力． 实验证明该方法是可行的．