Quantifying Dynamic Shapes in Soft Morphologies

Krishna Manaswi Digumarti*, Barry Trimmer, Andrew T. Conn, Jonathan Rossiter

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
248 Downloads (Pure)


Soft materials are driving the development of a new generation of robots that are intelligent, versatile, and adept at overcoming uncertainties in their everyday operation. The resulting soft robots are compliant and deform readily to change shape. In contrast to rigid-bodied robots, the shape of soft robots cannot be described easily. A numerical description is needed to enable the understanding of key features of shape and how they change as the soft body deforms. It can also quantify similarity between shapes. In this article, we use a method based on elliptic Fourier descriptors to describe soft deformable morphologies. We perform eigenshape analysis on the descriptors to extract key features that change during the motion of soft robots, showing the first analysis of this type on dynamic systems. We apply the method to both biological and soft robotic systems, which include the movement of a passive tentacle, the crawling movement of two species of caterpillar (Manduca sexta and Sphacelodes sp.), the motion of body segments in the M. sexta, and a comparison of the motion of a soft robot with that of a microorganism (euglenoid, Eutreptiella sp.). In the case of the tentacle, we show that the method captures differences in movement in varied media. In the caterpillars, the method illuminates a prominent feature of crawling, the extension of the terminal proleg. In the comparison between the robot and euglenoids, our method quantifies the similarity in shape to ∼85%. Furthermore, we present a possible method of extending the analysis to three-dimensional shapes.

Original languageEnglish
Pages (from-to)733-744
Number of pages12
JournalSoft Robotics
Issue number6
Early online date17 Jul 2019
Publication statusPublished - 12 Dec 2019

Structured keywords

  • Tactile Action Perception


  • Eigenshape analysis
  • quantifying shape
  • shape comparison


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