JOURNAL ARTICLE

From Behavior to Bio-Inspiration: Aerial Reorientation and Multi-Plane Stability in Kangaroo Rats, Computational Models, and Robots.

  • Published In: Integrative & Comparative Biology, 2024, v. 64, n. 3. P. 661 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chu, Xiangyu; Schwaner, M Janneke; An, Jiajun; Wang, Shengzhi; McGowan, Craig P; Au, Kwok Wai Samuel 3 of 3

Abstract

This article focuses on understanding how kangaroo rats use their tails for aerial stability and maneuverability during predator escape leaps and explores replicating this behavior with a simplified robotic tail. By combining three-dimensional kinematic data from free-ranging kangaroo rats, computational simulations using a two degrees of freedom (2-DoF) tail model, and experiments with a 2-DoF tailed robot, the study demonstrates that a lightweight, simplified tail can effectively control three-dimensional body orientation mid-air. The computational model, incorporating an offset between the tail base and body center of mass, successfully mimics the cyclic yaw motion patterns observed in kangaroo rats, while robotic experiments confirm the tail's ability to generate large yaw displacements and stabilize pitch and roll angles. This work advances the biomechanical understanding of kangaroo rat tail function and provides a foundational bio-inspired template for designing robotic devices capable of mid-air reorientation and stability.

Additional Information

  • Source:Integrative & Comparative Biology. 2024/09, Vol. 64, Issue 3, p661
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2024
  • ISSN:1540-7063
  • DOI:10.1093/icb/icae079
  • Accession Number:179960978
  • Copyright Statement:Copyright of Integrative & Comparative Biology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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