Dynamic Parameter Identification of a 7-DoF Lightweight Robot Manipulator Using Probabilistic Differential Optimization

The dynamic identification of a robot manipulator is a canonical problem in robotics and is usually solved by employing the least squares technique, considering the so-called base parameters of the robot. Such parameters, however, differ from those to which we really want to have access, such as the center of mass and the moments of inertia with respect to this center. Furthermore, the solution obtained by this technique can lead to unsuitable parameters from a physical point of view, which requires a later stage of physical feasibility analysis. In this article, we seek to overcome these drawbacks by identifying the parameters of a 7-DoF lightweight robot using only population-based metaheuristics. We make a study between different techniques and compare them with Bayesian optimization, another black-box approach. We also propose a new algorithm called Probabilistic Differential optimization and show that this algorithm is more efficient than the others considered and can find a physically feasible solution because of its probabilistic exploration ability. We also study the need for more than one excitation trajectory to overcome measurement noise and overfitting, and finally obtain accurate parameters of the Kinova Gen3 arm equipped with a Robotiq 2F-85 gripper.