Optimizing Out-of-Plane Stiffness for Soft Grippers

Su, Renbo; Tian, Yingjun; Du, Mingliang; Wang, Charlie C. L.

doi:10.1109/lra.2022.3193487

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…As a result computational soft gripper design research has focused on design exploration and optimisation within established gripper classes, such as jamming grippers [6,7] and pneumatic bending fingers. [29][30][31] However, several design toolkits have recently emerged which enable exploration of large design spaces using parameterised or implicit geometric descriptors. [32][33][34] Whilst vastly increasing tractable search space, these toolkits remain restricted to designs using hollow pneumatic chambers.…”

Section: Computational Gripper Designmentioning

confidence: 99%

Diversity‐Based Topology Optimization of Soft Robotic Grippers

Pinskier,

Wang,

Liow

et al. 2024

Advanced Intelligent Systems

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Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. Universal soft grippers have proven effective for grasping common objects, however complex objects or environments require bespoke gripper designs. Multi‐material printing presents a vast design‐space which, when coupled with an expressive computational design algorithm, can produce numerous, novel, high‐performance soft grippers. Finding high‐performing designs in challenging design spaces requires tools that combine rapid iteration, simulation accuracy, and fine‐grained optimization across a range of gripper designs to maximize performance, no current tools meet all these criteria. Herein, a diversity‐based soft gripper design framework combining generative design and topology optimization (TO) are presented. Compositional pattern‐producing networks (CPPNs) seed a diverse set of initial material distributions for the fine‐grained TO. Focusing on vacuum‐driven multi‐material soft grippers, several grasping modes (e.g. pinching, scooping) emerging without explicit prompting are demonstrated. Extensive automated experimentation with printed multi‐material grippers confirms optimized candidates exceed the grasp strength of comparable commercial designs. Grip strength, durability, and robustness is evaluated across 15,170 grasps. The combination of fine‐grained generative design, diversity‐based design processes, high‐fidelity simulation, and automated experimental evaluation represents a new paradigm for bespoke soft gripper design which is generalizable across numerous design domains, tasks, and environments.

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