Cyndia Cao — accepted AI-conference papers

2023

Walk-Burrow-Tug: Legged Anchoring Analysis Using RFT-Based Granular Limit Surfaces

Tae Myung Huh, Cyndia Cao, Jadesola Aderibigbe, Deaho Moon, Hannah S. Stuart

RA-L 2023

We develop a new resistive force theory based granular limit surface (RFT-GLS) method to predict and guide behaviors of forceful ground robots. As a case study, we harness a small mobile robotic system – MiniRQuad (296 g) – to ‘walk-burrow-tug;’ it actively exploits ground anchoring by burrowing its

Cited by 6 Source ↗Scholar ↗

2021

Granular Resistive Force Theory Implementation for Three-Dimensional Trajectories

Laura K. Treers, Cyndia Cao, Hannah S. Stuart

RA-L 2021

Modelling interaction forces as bodies intrude into granular media is a longstanding challenge in the design and control of machines that navigate and manipulate these highly complex materials. Granular Resistive Force Theory, or RFT, is a flexible, reduced-order model for predicting intrusion force

Cited by 23 Source ↗Scholar ↗