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

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. (C) 2016 Elsevier GmbH. All rights reserved.