The development of a genetic programming method for kinematic robot calibration

Kinematic robot calibration is the key requirement for the successful application of offline programming to industrial robotics. To compensate for inaccurate robot tool positioning, offline generated poses need to be corrected using a calibrated kinematic model, leading the robot to the desired poses. Conventional robot calibration techniques are heavily reliant upon numerical optimisation methods for model parameter estimation. However, the non-linearities of the kinematic equations, inappropriate model parameterisations with possible parameter discontinuities or redundancies, typically result in badly conditioned parameter identification. Research in kinematic robot calibration has therefore mainly focused on finding robot models and appropriate accommodated numerical methods to increase the accuracy of these models. This thesis presents an alternative approach to conventional kinematic robot calibration and develops a new inverse static kinematic calibration method based on the recent genetic programming paradigm. In this method the process of robot calibration is fully automated by applying symbolic model regression to model synthesis (structure and parameters) without involving iterative numerical methods for parameter identification, thus avoiding their drawbacks such as local convergence, numerical instability and parameter discontinuities. The approach developed in this work is focused on the evolutionary design and implementation of computer programs that model all error effects in particular non-geometric effects such as gear transmission errors, which considerably affect the overall positional accuracy of a robot. Genetic programming is employed to account for these effects and to induce joint correction models used to compensate for positional errors. The potential of this portable method is demonstrated in calibration experiments carried out on an industrial robot.