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Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations

机译：基于人工神经网络的运动学雅可比解的串行操纵器通过奇异配置

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摘要

Singularities and uncertainties in arm configurations are the main problems in kinematics robot control resulting from applying robot model, a solution based on using Artificial Neural Network (ANN) is proposed here. The main idea of this approach is the use of an ANN to learn the robot system characteristics rather than having to specify an explicit robot system model.rnDespite the fact that this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint for a six Degrees of Freedom (DOF) industrial robot. The network was designed to have one hidden layer, where the input were the Cartesian positions along the X, Y and Z coordinates, the orientation according to the RPY representation and the linear velocity of the end-effector while the output were the angular position and velocities for each joint, In a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained.rnThe resulting network was tested for a new set of data that has never been introduced to the network before these data were recorded in the singular configurations, in order to show the generality and efficiency of the proposed approach, and then testing results were verified experimentally.
机译：手臂结构的奇异性和不确定性是应用机器人模型所引起的运动学机器人控制中的主要问题，在此提出了一种基于人工神经网络的解决方案。这种方法的主要思想是使用ANN来学习机器人系统特性，而不必指定明确的机器人系统模型.rn尽管事实在实践中非常困难，但训练数据是通过固定在每个机器人上的传感器实验记录的六自由度（DOF）工业机器人的关节。该网络设计为具有一个隐藏层，其中输入是沿X，Y和Z坐标的笛卡尔位置，根据RPY表示的方向和端部执行器的线速度，而输出是角位置和每个关节的速度，在无障碍的工作空间中，获得了机械手关节空间中的离线平滑几何路径.rn对生成的网络进行了测试，以获取之前从未引入过网络的一组新数据这些数据以单一配置记录，以显示所提出方法的通用性和效率，然后通过实验验证测试结果。

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  • 来源
    《Advances in Engineering Software》 |2010年第2期|359-367|共9页
  • 作者

    Ali T. Hasan; N. Ismail; A.M.S. Hamouda; Ishak Aris; M.H. Marhaban; H.M.A.A. Al-Assadi;

  • 作者单位

    Department of Mechanical and Manufacturing Engineering, University Putra Malaysia, 43400UPM, Serdang Selangor, Malaysia;

    Department of Mechanical and Manufacturing Engineering, University Putra Malaysia, 43400UPM, Serdang Selangor, Malaysia;

    Department of Mechanical Engineering, Qatar University, Doha, Qatar;

    Department of Electrical and Electronic Engineering, University Putra Malaysia, 43400UPM, Serdang, Setangor, Malaysia;

    Department of Electrical and Electronic Engineering, University Putra Malaysia, 43400UPM, Serdang, Setangor, Malaysia;

    Department of Mechanical and Manufacturing Engineering, University Putra Malaysia, 43400UPM, Serdang Selangor, Malaysia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    neural networks; inverse kinematics; jacobian matrix; singularities; back propagation; robot control;

    机译：神经网络;逆运动学雅可比矩阵;奇点反向传播;机器人控制;

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