工业机器人智能运动学模型

Industry Robot Intelligent Kinematic Model

针对机器人运动学正、逆解推导过程复杂,计算量大的情况,提出了一种基于神经网络的机器人运动学正、逆解计算新方法。首先搭建了6自由度工业机器人实验平台,操纵机器人沿某一轨迹运动,记录下机器人在采样时刻的姿态角、坐标及关节角,获取实验数据。在此基础上,设计了一个三层神经网络,输入所采集到的数据进行训练,构建了机器人运动正反解神经网络模型。文章最后对所构建的模型进行验证,验证结果表明,由运动学模型所得到的预测值与实际的测量值误差小,模型具有较高的准确度。

To solve the problem that the robot＇s forward and inverse kinematics deducing process are very complicated and the calculation burden is very great, a new mothod for robot＇s forward and inverse kinentatics solution based on the neural network is proposed in it. Firstly, a 6-DOF industry robot platform was set up. Then the robot was manipulated along certain trajectory. The attitude angles, the coordinates and the joints＇ angles of robot were saved at sample time, so the experiment datas can be obtained. On this basis, a neural network contained three layers is setup, then the experiment datas are input to train the network. Therefore the neural network models for robot＇s forward and inverse kinematics are setup. In the end of it, several experiments are eaught out to test the models. The results showed that the perdietion values calculated by the models are fit to the real measured values, and the models have high accuracy.