Compliant Fins for Locomotion in Granular Media

Dongting Li, Sichuan Huang, Yong Tang, Hamidreza Marvi, Junliang Tao, Daniel M. Aukes

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In this letter, we present an approach to study the behavior of compliant plates in granular media and optimize the performance of a robot that utilizes this technique for mobility. From previous work and fundamental tests on thin plate force generation inside granular media, we introduce an origami-inspired mechanism with non-linear compliance in the joints that can be used in granular propulsion. This concept utilizes one-sided joint limits to create an asymmetric gait cycle that avoids more complicated alternatives often found in other swimming/digging robots. To analyze its locomotion as well as its shape and propulsive force, we utilize granular Resistive Force Theory (RFT) as a starting point. Adding compliance to this theory enables us to predict the time-based evolution of compliant plates when they are dragged and rotated. It also permits more rational design of swimming robots where fin design variables may be optimized against the characteristics of the granular medium. This is done using a Python-based dynamic simulation library to model the deformation of the plates and optimize aspects of the robot's gait. Finally, we prototype and test robot with a gait optimized using the modelling techniques mentioned above.

Original languageEnglish (US)
Article number9444211
Pages (from-to)5984-5991
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number3
DOIs
StatePublished - Jul 2021

Keywords

  • Soft robot materials and design
  • biologically-inspired robots
  • modeling, control, and learning for soft robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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