首页|New Robotics Findings Reported from Commonwealth Scientific and Industrial Research Organisation (CSIRO) (Diversity-based Topology Optimization of Soft Robotic Grippers)
New Robotics Findings Reported from Commonwealth Scientific and Industrial Research Organisation (CSIRO) (Diversity-based Topology Optimization of Soft Robotic Grippers)
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NETL
NSTL
Wiley
A new study on Robotics is now available. According to news reporting originating from Pullenvale, Australia, by NewsRx correspondents, research stated, “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.” Financial support for this research came from Science and Industry Endowment Fund. Our news editors obtained a quote from the research from Commonwealth Scientific and Industrial Research Organisation (CSIRO), “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. Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. A diversity-based soft gripper design framework combining generative design and topology optimization (TO) is presented.”
PullenvaleAustraliaAustralia and New ZealandEmerging TechnologiesMachine LearningRoboticsRobotsCommonwealth Scientific and Industrial Research Organisation (CSIRO)
NETL
NSTL
Wiley
