Robot calibration based on nonlinear formulation for Modular Reconfigurable Robots (MRRs)

Developed in this thesis is a full pose kinematic calibration method for modular reconfigurable robots (MRRs). This method is based on a nonlinear formulation as opposed to the conventional linear method that has a number of critical limitations. By avoiding linearization of the nonlinear robot forward kinematic equations, these nonlinear equations are directly used to identify the robot calibration parameters. A hybrid search method is developed to solve the nonlinear error equations by combining genetic algorithms with Monte Carlo simulations to ensure a global search over the robot workspace with good accuracy. A number of comparisons are made between the proposed method and the conventional linear method, indicating the advantages of the former over the latter by eliminating two critical limitations. The first one is the orthogonality sacrifice that leads to ill-conditioning of the Jacobian used in the linear method. The second one is quadrant sensitivity during the determination of the (Tait) Bryan angles from inverting the rotation matrix.