基于神經網絡的機器人視覺伺服控制

視覺伺服可以應用于機器人初始定位自動導引、自動避障、軌線跟蹤和運動目標跟蹤等控制系統(tǒng)中。傳統(tǒng)的視覺伺服系統(tǒng)在運行時包括工作空間定位和動力學逆運算兩個過程，需要實時計算視覺雅可比矩陣和機器人逆雅可比矩陣，計算量大，系統(tǒng)結構復雜。本文分析了基于圖像的機器人視覺伺服的基本原理，使用BP 神經網絡來確定達到指定位姿所需要的關節(jié)角度，將視覺信息直接融入伺服過程，在保證伺服精度的情況下大大簡化了控制算法。文中針對Puma560 工業(yè)機器人的模型進行了仿真實驗，結果驗證了該方
法的有效性。
關鍵詞： 視覺伺服； 圖像雅可比矩陣； 逆雅可比矩陣； BP 神經網絡； 視覺控制器
Abstract: 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.
Keywords: Visual servo; Image jacobian matrix; Inverse jacobian matrix; BP neural nerwork; Visual controller