Extracting feasible robot parameters from dynamic coefficients using nonlinear optimization methods

We consider the problem of extracting a complete set of numerical parameters that characterize the robot dynamics, starting from the identified values of dynamic coefficients that linearly parametrize the robot dynamic equations. This information is relevant when realistic dynamic simulations have to be performed using standard packages, or when addressing the efficient numerical implementation of model-based control laws using recursive Newton-Euler algorithms. The formulated problem is highly nonlinear and is solved through the use of global optimization techniques, while imposing also physical bounds on the dynamic parameters. The identification and parameter extraction process is illustrated and experimentally validated on the link dynamics of a KUKA LWR IV+ robot