Development of a calibrating algorithm for Delta Robot’s visual positioning based on artificial neural network

Abstract Delta robot with vision system can automatically control the end-actuator to accurately grasp moving objects on the conveyor belt. Establishment of the mapping relationship between the image feature space and the robot working space form a closed-loop chain for transformational link between the robot coordinate, camera coordinate and conveyor belt coordinate. The vision system calibration is a basic problem of robot vision research and implementation. The artificial neural networks (ANN) which has learning ability, adaptive ability and nonlinear function approximation ability can establish the nonlinear relationship between space points and pixel points to complete accurate calibration of the vision system. The convergence speed of calibration algorithm affects the real-time visual servo system. The calibration precision, generalization ability and calibration space of algorithm influence the robot grasping accuracy. Therefore, a new calibration technique for delta robot’s vision system was presented in this paper. The algorithm combines ANN with Faugeras vision system calibration technology. The setting of the initial value, network structure and the choice of the activation function is based on the model of Faugeras vision system calibration algorithm, which makes the actual output of the network closer to the target output. Experiments proved that this algorithm has higher calibration accuracy and generalization ability compared with the conventional calibration algorithm, as well as faster convergence speed compared with the conventional artificial neural network structure in the case of high calibration accuracy.

[1]  Gu Jinan,et al.  Neural network based visual servo control for CNC load/unload manipulator , 2015 .

[2]  Madhuri A. Chaudhari,et al.  Conjugate gradient back-propagation based artificial neural network for real time power quality assessment , 2016 .

[3]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Colin Bradley,et al.  Adaptive neural network visual servo control for dynamic positioning of underwater vehicles , 2015, Neurocomputing.

[5]  R. Y. Tsai,et al.  An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision , 1986, CVPR 1986.

[6]  Wenguo Li,et al.  Color calibration and correction applying linear interpolation technique for color fringe projection system , 2016 .

[7]  E. Baafi,et al.  Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine , 2016 .

[8]  Yongjin Kwon,et al.  Integrated remote control of the process capability and the accuracy of vision calibration , 2014 .

[9]  Weijie Li,et al.  BP neural network recognition algorithm for scour monitoring of subsea pipelines based on active thermometry , 2014 .

[10]  Li Wang,et al.  Camera self-calibration with lens distortion , 2016 .

[11]  Anton Satria Prabuwono,et al.  A linear model based on Kalman filter for improving neural network classification performance , 2016, Expert Syst. Appl..

[12]  Tomaz Tollazzi,et al.  Calibration of microsimulation traffic model using neural network approach , 2013, Expert Syst. Appl..

[13]  Maryam Abbasi Tarighat,et al.  Orthogonal projection approach and continuous wavelet transform-feed forward neural networks for simultaneous spectrophotometric determination of some heavy metals in diet samples , 2016 .

[14]  Gu Jinan,et al.  Research on the improvement of image edge detection algorithm based on artificial neural network , 2015 .

[15]  Bahram Gharabaghi,et al.  Entropy-based neural networks model for flow duration curves at ungauged sites , 2015 .

[16]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[17]  Fa-Long Luo,et al.  A homotopy method for training neural networks , 1998, Signal Process..

[18]  Antonije E. Onjia,et al.  Simplex optimization of artificial neural networks for the prediction of minimum detectable activity in gamma-ray spectrometry , 2006 .