Diversity‐Based Topology Optimization of Soft Robotic Grippers
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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Diversity‐Based Topology Optimization of Soft Robotic Grippers ; day:18 ; month:01 ; year:2024 ; extent:14
Advanced intelligent systems ; (18.01.2024) (gesamt 14)
- Creator
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Pinskier, Josh
Wang, Xing
Liow, Lois
Xie, Yue
Kumar, Prabhat
Langelaar, Matthijs
Howard, David
- DOI
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10.1002/aisy.202300505
- URN
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urn:nbn:de:101:1-2024011914233389987149
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:39 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Pinskier, Josh
- Wang, Xing
- Liow, Lois
- Xie, Yue
- Kumar, Prabhat
- Langelaar, Matthijs
- Howard, David
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