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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
Published on: February 4, 2018
Honeycomb Jamming: An Enabling Technology of Variable Stiffness Reconfiguration
Dickson Chun Fung Li1, Zerui Wang1, Jianshu Zhou1,2
1Department of Mechanical and Automation Engineering, T Stone Robotics Institute, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Abstract:
Jamming technologies are one of the promising approaches of variable stiffness mechanisms. However, there are problems limiting the broad application of jamming-based approaches such as a limited stiffening capacity and restricted stiffening position. This article presents a variable stiffness mechanism to achieve a rapid flexible to rigid state transition with biocompatibility, fail-safe design, and enhanced stiffening capacity. A novel strategy of reconfiguration of stiffening regions, which is entitled variable stiffness reconfiguration, is exploited to control not only the stiffnesses but also the positions and areas of the stiffening regions. At first, this article provides a new approach to the variable stiffness soft robotics community to enable both stiffness control and stiffening region adjustment. In this way, additional functions of the variable stiffness mechanisms including reproducing complex manipulator postures or customizing the soft gripper, through delivering functional units into or out of the devices, are demonstrated. Through reconfiguration, our design provides a generally applicable solution for a wide range of complex manipulator postures reproduced and objects grasped by reconfiguration of the stiffening regions. The variable stiffness mechanism is empirically evaluated with a comparison with other variable stiffness strategies in which the proposed solution shows greater stiffening capability, and an experimental search of optimal parameters of the honeycomb structure is presented. Finite element models, which have shown reasonable agreement with the empirical results, are constructed to model the stiffnesses, and an analytic model of the manipulator is derived to predict the posture.
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