Evidence map›Paper›PMID 39282249›Full record

ArticleFrontiers in robotics and AI2024

Robust self-propulsion in sand using simply controlled vibrating cubes.

Bangyuan Liu, Tianyu Wang, Deniz Kerimoglu, Velin Kojouharov, Frank L Hammond, Daniel I Goldman

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bangyuan Liu *Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, United States.
Tianyu Wang *Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, United States.
Deniz KerimogluSchool of Physics, Georgia Institute of Technology, Atlanta, GA, United States.
Velin KojouharovGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Frank L HammondGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Daniel I GoldmanInstitute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Much of the Earth and many surfaces of extraterrestrial bodies are composed of non-cohesive particulate matter. Locomoting on such granular terrain is challenging for common robotic devices, either wheeled or legged. In this work, we discover a robust alternative locomotion mechanism on granular media-generating movement via self-vibration. To demonstrate the effectiveness of this locomotion mechanism, we develop a cube-shaped robot with an embedded vibratory motor and conduct systematic experiments on granular terrains of various particle properties and slopes. We investigate how locomotion changes as a function of vibration frequency/intensity on such granular terrains. Compared to hard surfaces, we find such a vibratory locomotion mechanism enables the robot to move faster, and more stably on granular surfaces, facilitated by the interaction between the body and surrounding grains. We develop a numerical simulation of a vibrating single cube on granular media, enabling us to justify our hypothesis that the cube achieves locomotion through the oscillations excited at a distance from the cube's center of mass. The simplicity in structural design and controls of this robotic system indicates that vibratory locomotion can be a valuable alternative way to produce robust locomotion on granular terrains. We further demonstrate that such cube-shaped robots can be used as modular units for vibratory robots with capabilities of maneuverable forward and turning motions, showing potential practical scenarios for robotic systems.

Indexed as

DEM simulationgranular medialocomotionmodular robotrobophysicsrobotvibration

Identifiers

PMID39282249
PMCPMC11393480

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.