Regularization of inverse kinematics for redundant manipulators using neural network inversions
Regularization of inverse kinematics for redundant manipulators using neural network inversions
复制标题
使用神经网络反演对冗余机械手的逆运动学进行正则化
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
Koji Ito
中科院分区：
文献类型：
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作者：
Bao;Koji Ito
提出了一种利用神经网络逆正则化冗余度机器人逆运动学问题的新方法。这一方法分为四个阶段。在第一阶段，配置空间和相关的工作空间被划分成一组区域。在第二阶段，一组模块化神经网络在这些区域上采样的相关训练数据集上进行训练，以学习正向运动学函数。在第三阶段中，通过反转相应的模块化神经网络获得期望的末端执行器位置的多个逆运动学解。在第四阶段，根据给定的标准从多个解中选择“最优”逆运动学解。与现有方法相比，该方法的一个重要特点是既能找到多个解分支中的逆运动学解，又能找到属于同一解分支的逆运动学解，从而使用最优解对机械手的控制效果优于用普通解。这种方法用三关节平面臂来说明。
This paper presents a new approach to regularizing the inverse kinematics problem for redundant manipulators using neural network inversions. This approach is a four-phase procedure. In the first phase, the configuration space and associated workspace are partitioned into a set of regions. In the second phase, a set of modular neural networks is trained on associated training data sets sampled over these regions to learn the forward kinematic function. In the third phase, the multiple inverse kinematic solutions for a desired end-effector position are obtained by inverting the corresponding modular neural networks. In the fourth phase, an "optimal" inverse kinematic solution is selected from the multiple solutions according to a given criterion. This approach has an important feature in comparison with existing methods, that is, both the inverse kinematic solutions located in the multiple solution branches and the ones that belong to the same solution branch can be found. As a result, better control of the manipulator using the optimum solution than that using an ordinary solution can be achieved. This approach is illustrated with a three-joint planar arm.