A Force-Sensitive Grasping Controller Using Tactile Gripper Fingers and an Industrial Position-Controlled Robot

Abstract

Grasping fragile objects in the presence of un-certainty is a crucial task for robots, that becomes inherently challenging if the manipulator in use is an industrial robot platform that does not provide compliant control inputs. This requires not only to estimate the alignment error during object contact but also to alter the robot configuration to decrease this error while taking interaction constraints into account. Thus, this work proposes a novel grasping controller tailored to industrial robots by exploiting tactile sensor feedback on the robot gripper fingers in order to estimate and compensate for the alignment error when touching the object. Specifically, we propose two grasping strategies, that allow to either directly compensate for interaction wrenches or to solve a model predictive control-problem to minimize the estimated alignment error. Eventually, we outline how these modalities can be realized as a hybrid Cartesian force-velocity-controller on an industrial manipulator. We evaluate the proposed grasping strategies on a WSG 50 parallel two-finger gripper, that is equipped with a digital sensor array (DSA) per finger, for which we also provide an extended ROS-driver that allows to obtain DSA-data at a communication rate above 5 Hz. Given the collected empirical evidence, the presented grasping controller increases the skill-set of industrial robots in the presence of uncertainty and thus allows to apply stiff robots to handle fragile objects autonomously.