Motion Trajectory Correction Method for Force Reproduction in a Robot System
This study proposes a motion trajectory correction method for a robot based on iterative learning and force information to automate contact-based glue application tasks. To apply glue using a sponge, force information is essential because a uniform coating requires subtle changes in the contact force at the fingertips. Most studies on the automation of coating tasks focus on noncontact processes such as spray painting that do not require force sensing at the end effector. By contrast, we aim to automate contact-based glue application tasks using a robot-teaching system that matches the forces produced by a human operator and robot system. In a similar system, force in the pressing direction of the sponge was successfully reproduced. However, force errors remained along the other axes. Hence, in our system, the target motion trajectory is also corrected to match the torques about the two axes orthogonal to the pressing direction, improving the accuracy of the robot's movements. Experimental results demonstrate the effectiveness of the proposed method and its potential for use in contact-based coating tasks. In addition to the force and torque evaluation, a water-droplet spreading task on color-changing paper is used as a task-level surrogate assessment; applying the correction improved the spreading uniformity relative to no compensation.