Robot-dynamic calibration improvement by local identification

Notwithstanding the research on dynamic modelling of Industrial Robots (IRs hereafter) covers the last three decades, improvements are necessary to enable IRs adoption in technological tasks where high dynamics or interaction with environment is needed, e.g. deburring, milling, laser cutting etc. Indeed, this class of applications displays even more the necessity of high-accuracy tracking especially in workspace sub-regions, while common IR dynamic calibration methods often span the workspace at large (in term of positions and high velocities) resulting in an averagely fitting models. Open issues are therefore on the applicability/scalability of standard methods in workspace sub-regions and on the metrics used for the calibration performance evaluation. The paper proposes an algorithm designed to high-accuracy local dynamic identification, comparing it with the results achievable by a common IRs dynamic calibration method and by the same method scaled to a workspace sub-region. In addition, unlike from standard, the here reported experimental comparison is made by evaluating the torque prediction error for IRs robot moving along path programmed by standard/commercial IR motion planner and not along path belonging to the same template-class of trajectory used in identification phase.