Global Identification of Drive Gains Parameters of Robots Using a Known Payload

International audienceOff-line robot dynamic identification methods are based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint forces/torques that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). The joint forces/torques are calculated as the product of the known control signal (the current reference) by the joint drive gains. Then it is essential to get accurate values of joint drive gains to get accurate identification of inertial parameters. In the previous works, it was proposed to identify each gain separately. This does not allow taking into account the dynamic coupling between the robot axes. In this paper the global joint drive gains parameters of all joints are calculated simultaneously. The method is based on the total least squares solution of an over-determined linear system obtained with the inverse dynamic model calculated with available current reference and position sampled data while the robot is tracking one reference trajectory without load on the robot and one trajectory with a known payload fixed on the robot. The method is experimentally validated on an industrial Stäubli TX-40 robot

Global Identification of Drive Gains and Dynamic Parameters of Parallel Robots - Part 1: Theory

International audienceMost of the papers dealing with the dynamic parameters identification of parallel robots are based on simple models, which take only the dynamics of the moving platform into account. Moreover the actuator drive gains are not calibrated which leads to identification errors. In this paper a systematic way to derive the full dynamic identification model of parallel robots is proposed in combination with a method that allows the identification of both robot inertial parameters and drive gains

A model-based residual approach for human-robot collaboration during manual polishing operations

A fully robotized polishing of metallic surfaces may be insufficient in case of parts with complex geometric shapes, where a manual intervention is still preferable. Within the EU SYMPLEXITY project, we are considering tasks where manual polishing operations are performed in strict physical Human-Robot Collaboration (HRC) between a robot holding the part and a human operator equipped with an abrasive tool. During the polishing task, the robot should firmly keep the workpiece in a prescribed sequence of poses, by monitoring and resisting to the external forces applied by the operator. However, the user may also wish to change the orientation of the part mounted on the robot, simply by pushing or pulling the robot body and changing thus its configuration. We propose a control algorithm that is able to distinguish the external torques acting at the robot joints in two components, one due to the polishing forces being applied at the end-effector level, the other due to the intentional physical interaction engaged by the human. The latter component is used to reconfigure the manipulator arm and, accordingly, its end-effector orientation. The workpiece position is kept instead fixed, by exploiting the intrinsic redundancy of this subtask. The controller uses a F/T sensor mounted at the robot wrist, together with our recently developed model-based technique (the residual method) that is able to estimate online the joint torques due to contact forces/torques applied at any place along the robot structure. In order to obtain a reliable residual, which is necessary to implement the control algorithm, an accurate robot dynamic model (including also friction effects at the joints and drive gains) needs to be identified first. The complete dynamic identification and the proposed control method for the human-robot collaborative polishing task are illustrated on a 6R UR10 lightweight manipulator mounting an ATI 6D sensor

Dynamic Parameter Identification of a 6 DOF Industrial Robot using Power Model

International audienceOff-line dynamic identification requires the use of a model linear in relation to the robot dynamic parameters and the use of linear least squares technique to calculate the parameters. Most of time, the used model is the Inverse Dynamic Identification Model (IDIM). However, the computation of its symbolic expressions is extremely tedious. In order to simplify the procedure, the use of the Power Identification Model (PIM), which is dramatically simpler to obtain and that contains exactly the same dynamic parameters as the IDIM, was previously proposed. However, even if the identification of the PIM parameters for a 2 degrees-of-freedom (DOF) planar serial robot was successful, its fails to work for 6 DOF industrial robots. This paper discloses the reasons of this failure and presents a methodology for the identification of the robot dynamic parameters using the PIM. The method is experimentally validated on an industrial 6 DOF Stäubli TX-40 robot

Global Identification of Joint Drive Gains and Dynamic Parameters of Parallel Robots

International audienceOff-line robot dynamic identification methods are based on the use of the Inverse Dynamic Identification Model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal-the input reference of the motor current loop-with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of linear least squares technique to calculate the parameters (IDIM-LS technique). Most of the papers dealing with the dynamic parameters identification of parallel robots are based on simple models, which take only the dynamics of the moving platform into account. However, for advanced applications such as output force control in which the robot interaction force with the environment are estimated from the values of the input reference, both identifications of the full robot model and joint drive gains are required to obtain the best results. In this paper a systematic way to derive the full dynamic identification model of parallel robots is proposed in combination with a method that allows the identification of both robot inertial parameters and drive gains. The method is based on the total least squares solution of an over-determined linear system obtained with the inverse dynamic model. This model is calculated with available input reference of the motor current loop and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on a prototype of parallel robot, the Orthoglide

Identification dynamique des robots à flexibilités articulaires

This work is the result of collaboration between IRCCyN and ONERA on dynamic identification of robots with joint flexibilities, used for example in new applications for collaborative robotics. The usual identification technique in robotics requires the actual data of motor positions and the actual data of elastic deformations, which are usually not available in industrial robots. Recently, a new technique called DIDIM (Direct and Inverse Dynamic Identification Models), which uses only the data of motor torques, has been proposed and validated on rigid robots. This thesis proposes an extension of DIDIM, which uses no actual position data at all, to the case of robots with joint flexibilities. First, a comparative study on a rigid 6-axis robot with 61 parameters, shows the superiority of DIDIM over a conventional method CLOE (Closed- Loop Output Error) in position: DIDIM converges 100 times faster and is strongly more robust with respect to errors in the initial conditions. Second, DIDIM is extended to robots with joint flexibilities in a three steps procedure: a rigid model identification at low frequencies, an approximated identification of the flexible mode and of the inertia of each side of the flexibility, and finally the overall accurate identification of the full flexible dynamic model. A first experimental validation is performed on a test bench robot with one joint and one flexibility. A second validation in simulation on the 7 axes Kuka Light Weight Robot shows the effectiveness of DIDIM applied to industrial robots with joint flexibilities, in the case where the actual control law is known.Ce travail résulte d'une collaboration entre l'IRCCyN et l'ONERA sur l'identification dynamique des robots à flexibilités articulaires, utilisés par exemple dans les applications de la robotique collaborative. La technique d'identification usuelle en robotique nécessite la mesure des positions moteurs et la mesure des déformations élastiques, non disponibles sur les robots industriels. Récemment, une nouvelle technique nommée DIDIM (Direct and Inverse Dynamic Identification Models), qui utilise uniquement la mesure des efforts moteurs, a été proposée et validée sur les robots rigides. Cette thèse propose une extension de DIDIM, qui n'utilise aucune mesure de position, aux cas des robots à flexibilités articulaires. On réalise d'abord une étude comparative sur un robot rigide 6 axes avec 61 paramètres, qui démontre la supériorité de DIDIM sur une méthode usuelle en boucle fermée à erreur de sortie en position (CLOE) : DIDIM converge 100 fois plus vite et est largement plus robuste vis à vis des erreurs sur les conditions initiales.Ensuite DIDIM est étendue aux robots à flexibilités articulaires avec une procédure en trois étapes : une identification du modèle rigide en basses fréquences, une identification du mode flexible et des inerties de part et d'autre de la flexibilité et enfin une identification globale précise du modèle dynamique flexible complet. Une validation expérimentale est réalisée sur un banc d'essai de robot un axe avec une flexibilité. Ensuite, une validation en simulation sur le robot 7 axes Kuka Light Weight Robot montre l'efficacité de la méthode DIDIM appliquée aux robots à flexibilités articulaires industriels, dans le cas où la commande est connue