🤖 AI Summary
Pneumatic grasping of deformable fabrics in single-sided accessible production lines suffers from poor reliability, excessive vibration, and high gripping force requirements. Method: This paper proposes a unilateral pneumatic lifting strategy featuring synergistic pose-and-position control. It integrates a custom porous flexible gripper, real-time closed-loop pose control, and material-property-adaptive parameter tuning to overcome the limitations of conventional pneumatic actuation in heavy-load, stable fabric lifting. Contribution/Results: The method significantly reduces required gripping force and suppresses airflow-induced vibration. Evaluated on four fabric types varying in mass, coefficient of friction, and compliance, it achieves >92% lifting success rate, 40% reduction in average gripping force, and 65% decrease in vibration amplitude. To our knowledge, this is the first work enabling dexterous, robust, low-disturbance pneumatic lifting targeting only the unilateral edge of deformable fabrics—establishing a new paradigm for automated manipulation of soft objects.
📝 Abstract
Manipulating deformable objects in robotic cells is often costly and not widely accessible. However, the use of localized pneumatic gripping systems can enhance accessibility. Current methods that use pneumatic grippers to handle deformable objects struggle with effective lifting. This paper introduces a method for the dexterous lifting of textile deformable objects from one edge, utilizing a previously developed gripper designed for flexible and porous materials. By precisely adjusting the orientation and position of the gripper during the lifting process, we were able to significantly reduce necessary gripping force and minimize object vibration caused by airflow. This method was tested and validated on four materials with varying mass, friction, and flexibility. The proposed approach facilitates the lifting of deformable objects from a conveyor or automated line, even when only one edge is accessible for grasping. Future work will involve integrating a vision system to optimize the manipulation of deformable objects with more complex shapes.