A benchmark for identification of a flexible serial manipulator using a camera

Abstract This paper presents a benchmark dedicated to the identification of a flexible manipulator using a camera. Robotic systems are usually identified using joint-level data (torque and position). However, in the current framework of vision-based control, the model must be able to predict the position of the instrument provided by the camera. Joint positions are also provided as inner variables and can also be used to improve the accuracy of the identified model. One additional complexity source is the presence of flexible modes that need to be accounted for in order to obtain a high bandwidth in control. The proposed simplified case with two inputs and two outputs is considered as sufficient to deal with the non-linear flexible nature of the system. This benchmark is likely to suit for LPV identification methods.