基于神经网络的机器人视觉伺服控制

视觉伺服可以应用于机器人初始定位自动导引、自动避障、轨线跟踪和运动目标跟踪等控制系统中。传统的视觉伺服系统在运行时包括工作空间定位和动力学逆运算两个过程,需要实时计算视觉雅可比矩阵和机器人逆雅可比矩阵,计算量大,系统结构复杂。本文分析了基于图像的机器人视觉伺服的基本原理,使用BP神经网络来确定达到指定位姿所需要的关节角度,将视觉信息直接融入伺服过程,在保证伺服精度的情况下大大简化了控制算法。文中针对Puma560工业机器人的模型进行了仿真实验,结果验证了该方法的有效性。

Design of Robotic Visual Servo Controll Based on Neural Network

Visual servo system can be used in the control systems of robot original orientation guiding, obstacle avoiding, trajectory tracking and moving object tracking, etc. During working, the traditional visual servo system consists of two processes: determination of the workpiece position and inverse kinetic calculation. So real- time computation of visual Jacobian and inverse Jacobian of the robot have been needed. Both the computation and the structure of the system are complex. In this paper, the basic principle of robot visual servo system is analyzed. A BP neural network is proposed to determine the required joint angles for the set position and orientation. This method can integrate visual data directly into the servo process, so under the condition that the servo precision is ensured, the computation of the control arithmetic is greatly simplified. The simulation experiment for Puma560 robot is made and simulation results proved the effectiveness of the method.