Optimal selection of measurement configurations for robot calibration using simulated annealing

Measuring robot positions and orientations is a crucial step in a robot calibration process. Off-line optimal selection of measurement configurations can significantly improve the accuracy of kinematic identification. Since the dimension of the parameter space is very large and the cost function is highly nonlinear, this selection process could be well beyond the capacity of today´s computers if a global optimal solution is sought by an exhaustive search. On the other hand, gradient-based algorithms are often trapped into local minima. A simulated annealing (SA) approach is adopted in this paper to obtain optimal or near optimal measurement configurations for robot calibration. Simulated annealing is capable of overcoming local minimum points. It is also very convenient for the inclusion of joint travel limits. The SA algorithm is costly computationally; however, since optimal configuration selection can be performed off-line, this may not be a serious problem. To accelerate the convergence rate, a suitable cooling schedule is devised. Practical implementation considerations are discussed. Experimental results are presented to demonstrate the feasibility of the proposed approach