Robotic flytrap with an ultra-sensitive 'trichome' and fast-response 'lobes'.

Journal: Bioinspiration & biomimetics
PMID:

Abstract

Nature abounds with examples of ultra-sensitive perception and agile body transformation for highly efficient predation as well as extraordinary adaptation to complex environments. Flytraps, as a representative example, could effectively detect the most minute physical stimulation of insects and respond instantly, inspiring numerous robotic designs and applications. However, current robotic flytraps face challenges in reproducing the ultra-sensitive insect-touch perception. In addition, fast and fully-covered capture of live insects with robotic flytraps remains elusive. Here we report a novel design of a robotic flytrap with an ultra-sensitive 'trichome' and bistable fast-response 'lobes'. Our results show that the 'trichome' of the proposed robotic flytrap could detect and respond to both the external stimulation of 0.45 mN and a tiny touch of a flying bee with a weight of 0.12 g. Besides, once the 'trichome' is triggered, the bistable 'lobes' could instantly close themselves in 0.2 s to form a fully-covered cage to trap the bees, and reopen to set them free after the tests. We introduce the design, modeling, optimization, and verification of the robotic flytrap, and envision broader applications of this technology in ultra-sensitive perception, fast-response grasping, and biomedical engineering studies.

Authors

  • Yongkang Jiang
    Institute of Robotics, Beihang University, Beijing, People's Republic of China. Reconfigurable Robotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Yingtian Li
    Department of Mechanical Engineering, The University of Hong Kong , Hong Kong, China .
  • Xin Tong
    Department of Data Sciences and Operations, Marshall Business School, University of Southern California.
  • Zhipeng Wang
    Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai, 200003, PR China.
  • Yanmin Zhou
    State Key Laboratory of Intelligent Autonomous Systems, Shanghai, 201210, China. yanmin.zhou@tongji.edu.cn.
  • Bin He
    Clinical Translational Medical Center, The Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan, Guangdong, China.