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首页> 外文期刊>International Journal of Advanced Robotic Systems >Performance Prediction Network for Serial Manipulators Inverse Kinematics Solution Passing Through Singular Configurations
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Performance Prediction Network for Serial Manipulators Inverse Kinematics Solution Passing Through Singular Configurations

机译:通过奇异配置的串行机械手逆运动学解决方案性能预测网络

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摘要

This paper is devoted to the application of Artificial Neural Networks (ANN) to the solution of the Inverse Kinematics (IK) problem for serial robot manipulators, in this study two networks were trained and compared to examine the effect of considering the Jacobian Matrix to the efficiency of the IK solution.Given the desired trajectory of the end effector of the manipulator in a free-of-obstacles workspace, Offline smooth geometric paths in the joint space of the manipulator are obtained. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility and backlashes in gear trainThe generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six DOF serial robot manipulator, finally the obtained results have been verified experimentally.
机译:本文致力于将人工神经网络(ANN)用于解决串行机器人操纵器的逆运动学(IK)问题,在此研究中,对两个网络进行了训练并进行了比较,以检验考虑雅可比矩阵对机器人的影响。鉴于IK解决方案的效率,鉴于在无障碍工作空间中机械手的末端执行器的期望轨迹,可以获得机械手关节空间中的离线平滑几何路径。尽管在实践中非常困难,但本研究中使用的数据是通过固定在机器人关节上的传感器进行实验记录的,以克服现实世界中运动学不确定性的影响,例如不确定的连杆参数,连杆的柔韧性和齿轮系中的间隙。通过对通用的六自由度串行机器人操纵器的仿真,证明了该算法的通用性和有效性,最后通过实验验证了所得结果。

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