We consider the conventional techniques of vision robot system calibration where camera parameters and robot hand–eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand–eye calibration based on the calibrated parameters of cameras. In this paper we propose a joint algorithm that combines the camera calibration and the hand–eye calibration together. The proposed algorithm gives the solutions of the cameras' parameters and the hand–eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm. We also apply our algorithm in the real application of the robot-assisted surgical system, and very good results have been obtained.

This work describes a hierarchically structured geometric database in an off-line robot programming system. The data structure contains the numerical definition of the frame variables, as well as an indicator of the respective reference frames. Moreover, the physical relations between the objects in the environment are included. The database is implemented such that it continuously reflects the actual structure of the environment. As a result, all calculations of the frame locations are carried out automatically. Moreover, the programming system is capable to autonomously updating the numerical information after changes in the environment. Making this database the heart of a robot programming system greatly simplifies the off-line programming of complex robot tasks, like f.i. assembly tasks.

In this paper neural network (NN) control techniques for non-model based PD controlled robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique is that compensation of robot dynamics uncertainties is done outside the control loop by modifying the desired input trajectory. By using different NN training signals, two NN control schemes are developed. One is comparable to that in the FEL technique and another has to deal with the Jacobian of the PD controlled robot dynamic system. Performances of both controllers for various trajectories with different PD controller gains are examined and compared with that of the FEL controller. It is shown that the new control technique performed better and robust to PD controller gain variations.