skip to main content

Search for: All records

Creators/Authors contains: "Rhodes, T."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Robustness, compactness, and portability of tensegrity robots make them suitable candidates for locomotion on unknown terrains. Despite these advantages, challenges remain relating to simplicity of fabrication and locomotion. The paper introduces a design solution for fabricating tensegrity robots of varying morphologies with modular components created using rapid prototyping techniques, including 3D printing and laser-cutting. % It explores different robot morphologies that attempt to balance structural complexity while facilitating smooth locomotion. The techniques are utilized to fabricate simple tensegrity structures, followed by tensegrity robots in icosahedron and half-circle arc morphologies. Locomotion strategies for such robots involve altering of the position of center-of-mass to induce `tip-over'. Furthermore, the design of curved links of tensegrity mechanisms facilitates continuous change in the point of contact (along the curve) as compared to piece-wise continuous in the traditional straight links (point contact) which induces impulse reaction forces during locomotion. The resulting two tensegrity robots - six-straight strut icosahedron and two half-circle arc morphology - achieve locomotion through internal mass-shifting utilizing the presented modular mass-shifting mechanism. The curve-link tensegrity robot demonstrates smooth locomotion along with folding-unfolding capability.
  2. Granular materials produce audio-frequency mechanical vibrations in air and structures when manipulated. These vibrations correlate with both the nature of the events and the intrinsic properties of the materials producing them. We therefore propose learning to use audio-frequency vibrations from contact events to estimate the flow and amount of granular materials during scooping and pouring tasks. We evaluated multiple deep and shallow learning frameworks on a dataset of 13,750 shaking and pouring samples across five different granular materials. Our results indicate that audio is an informative sensor modality for accurately estimating flow and amounts, with a mean RMSE of 2.8g across the five materials for pouring. We also demonstrate how the learned networks can be used to pour a desired amount of material.