Robot dynamic model identification through excitation trajectories minimizing the correlation influence among essential parameters

Robot dynamics is commonly modeled as a linear function of the robot kinematic state from a set of dynamic parameters into motor torques. Base parameters (i.e. the set of theoretically demonstrated linearly-independent parameters) can be reduced to a subset of “essential” parameters by eliminating those that are negligible with respect to their contribution in motor torques. However, generic trajectories, if not properly defined, couple the contribution of such essential parameters into the motor torques, actually reducing the estimation accuracy of the dynamics parameters. The work presented here introduces an index for evaluating correlation influence among essential parameters along an executed trajectory. Such index is then exploited for an optimal search of excitatory patterns consistent with the kinematical coupling constraints. The method is experimentally compared with the results achievable by one of the most popular IRs dynamic calibration method.