POSES SELECTION USING GENETIC ALGORITHM TO IMPROVE THE LOCAL POE KINEMATICS CALIBRATION

This paper investigates the use of genetic algorithm to optimize poses selection to improve kinematic calibration for manipulator. Genetic algorithm is used to determine the optimal poses while iterative least square algorithm is used to calibrate the kinematics model of the manipulator. Observability index are used to evaluate the optimality of the set of poses. The fitness function of genetic algorithm is chosen from the observability index. In addition, local POE (Product of Exponential) method is used to model the manipulator kinematics. The objective of this paper is to design an algorithm which optimizes the number of poses while improving the calibration performance. The experiments utilize 7-DOF Mitsubishi PA-10 manipulator as the platform and a LEICA laser tracker as the measurement tool. The experiment shows that genetic algorithm can optimize the number of poses and improve the calibration performance.