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机器人模型
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  robot model
     Design of unactuated joint robot model
     非驱动关节机器人模型的设计与实现
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     A new robot model reference adaptive control method based on neural networks is presented.
     提出一种新的基于神经网络的机器人模型参考自适应控制方法。
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     Design of Automatic Line Tracing Transport Robot Model
     自动跟踪运输机器人模型的设计
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     Then we choose PUMA560 as the robot model to wash the window and describe the robot in the space using the method of D-H.
     接着选取PUMA560机器人作为擦玻璃的机器人模型,采用D-H法对PUMA560机器人的结构参数以及在空间的位置进行了描述;
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     A NEW ROBOT MODEL AND ASSOCIATED CONTROL ALGORITHM
     一种新的机器人模型及其控制算法
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  “机器人模型”译为未确定词的双语例句
     Theoretical and Experimental Study on Model Identification of Spatial 7R Robots Based on Neural Networks
     基于神经网络的空间7R机器人模型辨识及其实验研究
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     Study on Model Error and Calibration Method of the Measuring Robot
     测量机器人模型误差及标定方法的研究
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     On the Model Identification and Control of Robots Based on Neural Networks
     基于神经网络的机器人模型辨识与控制研究
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     Model Reference Adaptive Control of Robot Manipulators Based on Neural Network Technique
     基于神经网络的机器人模型参考自适应控制的研究
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     Model of intelligent cleaning robot based on behavior evolution
     基于行为进化的智能保洁机器人模型
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  相似匹配句对
     A New Method for Dynamic Modeling of the Robots
     机器人动力学模型新方法
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     S. models have been found.
     S.模型
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     Obviously, EVA model turns out a scientific appraisals to motivate the managers.
     该模型为:
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     Robot
     机器人
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     Programming for Dynamical Models of Robot
     机器人动力学模型的程序实现
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  robot model
In the robot model used, the current curvature of the trajectory of the objective point which is related by simple algebraic expressions with the angle of rotation of the front wheels was taken as the control parameter.
      
Using this method all forward solutions of a new parallel robot model which was put forward lately by Robot Open Laboratory of Science Institute of China were obtained.
      
Using this method all forward solutions of a new parallel robot model which was put forward lately by Robot Open Laboratory of Science Institute of China were obtained.
      
In this paper, we present a robot model for visually guided reaching and grasping.
      
This paper aims to improve the controller's robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level.
      
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The author gives a computer model of intentional goal-seeking, which has two parts. The static part consists of a goalrepresentation and a plan for achieving it. The dynamic part is arepeated feedback cycle of sensed input, internal information processingand action output. Human intentionality is accompanied by consciousness,but there could be an intentional robot which is not conscious. Using robotic models of goal-seeking systems, we define a teleologicalcontinuum of goal-seeking systems; this continuum runs...

The author gives a computer model of intentional goal-seeking, which has two parts. The static part consists of a goalrepresentation and a plan for achieving it. The dynamic part is arepeated feedback cycle of sensed input, internal information processingand action output. Human intentionality is accompanied by consciousness,but there could be an intentional robot which is not conscious. Using robotic models of goal-seeking systems, we define a teleologicalcontinuum of goal-seeking systems; this continuum runs from simpledirect response systems to intentional systems. There are two traditionalexplanations of this teleological continuum: the final cause theory ofPlato, Aristotle, and Plotinus, and the reductionist theory of the Greekatomists and modern evolutionary biologists. The writer comments onthese doctrines. The author surveys several types of conscious functioning andproposes a theory of consciousness that is automaton based, involvingcomparisons between the human person, with mind and body, and theorganization of a computer having a central control. Viewed from theperspective of computer architecture, human consciousness is a particularkind of computer control system, a relatively simple real-time controlwhich, when the system is awakened, directs short-term activities andplans longer-term activities.

本文提出了一个意向性目标搜寻的计算机模型,它有两个部分,静态部分包括一个目标表征和到达这个目标的程序。动态部分是一个感觉输入、中间信息加工,行为输出的重复的反馈循环。人类意向必然伴有意识,但机器人则可能无意识而有意向。 利用目标搜寻系统机器人模型,我们对目标搜寻系统的目的连续统下了定义;这个连续统从简单的直接反应系统一直到意向性系统。对这个目的连续统有两种传统的解释:一是柏拉图、亚里士多德和普罗提诺的终极原因理论,二是希腊原子论者和现代进化生物学家的还原主义学说。本文评价了这些学说。 本文考察了几种类型的意识机能,提出了以自动机为依据的意识理论,比较了人类和计算机。从计算机结构的角度来看,人类意识是一类特殊的计算机控制系统。

This paper studies the path tracking control problem of robot manipulator in the presence ofuncertainty.A robust computde torque control strategy is presented.It is shown that with this strategy,thetracking convergence is ensured.

本文研究了存在扰动和参数不确定因素时机器人的轨迹跟踪控制问题.给出了一种鲁棒计算力矩控制方案,它由改进力矩发生器和鲁棒补偿器组成,该方案具有下述特点:①不依赖精确机器人模型;②容忍一定程度的计算误差。这给模型简化、减轻控制计算机运算负担、缩短运算时间提供了一条途径;③保证被控机器人具有渐近跟踪理想轨迹的能力.这些结论通过仿真实验已初步得到证实.

The control of RQV's motion is studied.The model of ROV is non-linear and strong coupling, and it is very difficult to obtain satisfied control property with classical control method.A new method dealing with the problem of non-linear system control is mentioned.It is inverse system method that the original non-linear system can be decoupl-ing controlled through dynamic compensation using inverse system.The method is used in ROV's control, and the result of simulation is given.

本文研究水下机器人的航行控制问题,水下机器人模型具有高度的非线性和强耦合,用古典的控制方法对其进行高质量的控制很难.本文将非线性系统综合的一种新方法——逆系统动态补偿解耦控制应用到水下机器人的航行控制中,并给出了理想的仿真结果.

 
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