首页> 外文会议>2002 ASME International Mechanical Engineering Congress and Exposition , Nov 17-22, 2002, New Orleans, Louisiana >TEMPORAL DIFFERENCE APPROACH TO COORDINATED MOTION CONTROL OF COOPERATING TWO LINK ROBOTS
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TEMPORAL DIFFERENCE APPROACH TO COORDINATED MOTION CONTROL OF COOPERATING TWO LINK ROBOTS

机译:两连杆机器人协调运动控制的时间差异方法

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To perform complex manufacturing operations, two or more manipulators are made to work in concert. When robots work independently of other robots, small errors made (e.g., due to inaccuracies in modeling of the manipulator) by individual robots may be acceptable. However, when robots work together, then high precision is required. This calls for the use of adaptive controllers in order to minimize errors. This paper discusses the application of Temporal Difference Learning (TDL) method, wherein stiffness of manipulator is adapted based on the feedback obtained from force/torque sensors. In order to accomplish this, simulation was carried out by adding feedback (force and change of force) to controllers, so that required trajectory could be adhered. Normally this error (deviation from required trajectory) occurs due to non-availability of the correct values of stiffness of the system. Stiffness of system is difficult to calculate due to inherent complexities in formulating an accurate dynamic model of system. Variation in parameters, for example change of friction due to aging, change of moment of inertia due to changes in payload position and orientation, significantly affect the dynamic model of manipulator. One of the ways to achieve compliance is by updating the dynamic model of the system. The other way is to use the external control loop which provide manipulator with set-points such that the desired compliance can be achieved. This paper demonstrates the appropriateness of TDL method in updating the dynamic model of the system. This updated model is then used to calculate the torques of the joints. As the process of learning converges, the function learned represents a nearly perfect model of the stiffness of the system.
机译:为了执行复杂的制造操作,需要使两个或更多个机械手协同工作。当机器人独立于其他机器人工作时,单个机器人造成的小错误(例如,由于操纵器建模不准确)可能是可以接受的。但是,当机器人一起工作时,则需要高精度。这就要求使用自适应控制器,以最大程度地减少错误。本文讨论了时差学习(TDL)方法的应用,其中基于从力/扭矩传感器获得的反馈来调整机械臂的刚度。为了做到这一点,通过向控制器添加反馈(力和力的变化)来进行仿真,从而可以遵循所需的轨迹。通常,由于无法获得系统刚度的正确值,因此会发生此错误(偏离所需轨迹)。由于在建立精确的系统动态模型时固有的复杂性,很难计算系统的刚度。参数的变化(例如,由于老化引起的摩擦力变化,由于有效载荷位置和方向的变化引起的惯性矩的变化)会显着影响机械手的动力学模型。实现合规性的方法之一是通过更新系统的动态模型。另一种方法是使用外部控制回路,该回路为操纵器提供设定点,从而可以实现所需的顺应性。本文说明了TDL方法在更新系统动态模型中的适用性。然后,使用此更新的模型来计算关节的扭矩。随着学习过程的收敛,所学习的函数代表了系统刚度的近乎完美的模型。

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