Dynamic Parameter Estimation Utilizing Optimized Trajectories

Abstract

We suggest a procedure for dynamic parameter estimation of serial robot manipulators. Its basic idea relies on the synthesis of an optimal manipulation trajectory, which is based on properly introduced parameter aggregates to ensure a collection of numerically well-conditioned data-sets, yielding an accurate computation of parameter estimates. The optimal trajectory itself is computed by using a memetic algorithm, which represents a metaheuristic combination of genetic and gradient based algorithms. The algorithm is experimentally verified by estimating the parameters of the UR5 robot by Universal Robots.