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

Abstract

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 re-$ cently 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. polishing 45 placed on the final robot flange provides then the exchanged forces/torques. When the contact point is known in advance, the problem of sensor-less contact force estimation without the use of a F/T sensor has been addressed in [13], based on motor torque/current measurements. A dithering feedforward torque

On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

“…The latter was used during the experiments. Gaz et al [20] presented a new robot control algorithm aimed at being used in a scenario where a robot grasps a piece while the operator polishes it. The proposed collaborative task was the same we used, but they considered only two robot modes: (a) stiffness, while the user polishes' and (b) compliance, while the user modifies the orientation of the end effector.…”

Human-robot cooperation for robust surface treatment using non-conventional sliding mode control

“…where σ 1 is a column vector composed of all equality constraints σ 1,i , K 1 is a diagonal matrix composed of all the approaching parameters K eq,i , see (22), K t,s…”

“…Recently, a human-robot collaboration aimed at manual polishing operations was presented in [22]. In this application the robot holds the workpiece and the human operator is assumed equipped with an abrasive tool to perform the polishing operation.…”

Finite-Time Disturbance Observer for Robotic Manipulators

“…In order to deduce these uncertain disturbances induced by either internal or external factors, it is necessary to incorporate a disturbance observer (DOB) to estimate these disturbances. Once the observer is provided, the DOB in robotic manipulators can enable versatile applications including disturbance observer based control [1,2,3], friction estimation and compensation [4,5], sensorless force/torque control [6,7,8], fault diagnose and isolation (FDI) [9,10,11] etc. For example, the interaction force between human and robot could be treated as the joint torque disturbances as the Cartesian forces could be projected into the joint level.…”

“…GMO is able to realize FDI such as the prediction of the accidental impact as well as the saturated actuator fault [9]. Its easy implementation and reliable performance make GMO a popular and widely used method in many robotic applications [6,8,12]. However, in practice, GMO has to make a trade-off between the response rate and noise filtering.…”