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Design and Development of a Soft Robotic Gripper for Fabric Material Handling

Bowen Wang

发表年份
2020
引用次数
2
访问权限
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摘要

Fabric and textile materials are widely used in many industrial applications, especially in automotive, aviation and consumer goods. Currently, there is no semi-automatic or automatic solution for rapid, effective, and reconfigurable pick and place activities for limp, air permeable flexible components in industry. The production of these light-weight flexible textile or composite fiber products highly rely on manual operations, which lead to high production costs, workplace safety issues, and process bottlenecks. As a bio-inspired novel technology, soft robotic grippers provide new opportunities for the automation of fabric handling tasks. In this research, the characteristics of fabric pick and place tasks using the clamping grippers are quantitatively investigated. Experiments on a carbon fiber fabric are performed with a collaborative robot to explore the damage, slippage, draping, and wrinkling during basic pick and place operations. Based on the experimental results, multiple soft robotic gripper configurations are developed, including a compliant glove set that can improve the performance of traditional rigid grippers, an elastomer-based soft gripper, and a linkage-based underactuated gripper. The gripper designs are analyzed and refined based on finite element simulation. Prototypes of the grippers are fabricated using a rapid tooling solution for an overmolding strategy to verify their functionality. Through the research, it is proven feasible to reliably perform flexible fabric handling operations using soft grippers with appropriate toolpath planning. Finite element simulation and additive manufacturing have shown to be useful tools during the gripper design and development procedure, and the methodologies developed and applied in this work should be expanded for more flexible material handling challenges.

关键词

Soft roboticsGrippersComputer scienceRobotEngineeringMechanical engineeringArtificial intelligence

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