Stiffness Preprogrammable Soft Bending Pneumatic Actuators for High-Efficient, Conformal Operation.

Journal: Soft robotics
Published Date:

Abstract

Soft pneumatic actuators (SPAs) are extensively investigated due to their simple control strategies for producing sophisticated motions. However, the motions or operations of homogeneous SPAs show obvious limitations in some varying curvature interaction scenarios because of the profile mismatch of homogeneous SPAs and specific interacted objects. Herein, a stiffness preprogrammable soft pneumatic actuator (SPSPA) is proposed by discretely presetting gradient geometrical or materials distributions. Through finite element analysis and experimental validation, a mathematical model of behavior prediction of SPSPA was built to relate the geometrical parameters/materials with its morphing behaviors, making it possible to reversely obtain designed parameters. This design strategy enables conformal and efficient interaction in some curvature varying scenarios. Specifically, higher effective contact area, perimeter utilization ratio, and conformal ability can be obtained while interacting with those inhomogeneous curvature objects, for example, more than 434.7% improvement in contact area rates and 12.5% enhancement in perimeter utilization ratios toward a typical equilateral triangle object. Further, a serial of SPSPAs that have conformal grasping/interactive capability, better contact sensing behaviors were demonstrated. For example, an SPSPA and an SPSP robot were demonstrated, which showed better kinetic, kinematic characterizations and sensing capability compared with the homogeneous one while coming across varying curvature objects. Moreover, underactuated finger rehabilitation SPSPAs were demonstrated with customized profiles and coupled joint motion. This customized scheme can be potentially used in those specific-purposed, single, and repetitive application scenarios where varying curvature, conformal and efficient interaction are needed.

Authors

  • Xingxing Ke
    School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
  • Jiajun Jang
    State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Zhiping Chai
    State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Haochen Yong
    State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Jiaqi Zhu
    State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Han Chen
    School of Statistics, University of Minnesota at Twin Cities.
  • Chuan Fei Guo
    Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518000, China.
  • Han Ding
    State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Zhigang Wu
    State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.