Portrait de Audrey Sedal

Audrey Sedal

Membre académique associé
Professeure adjointe, McGill University, Département de l'ingénierie médicale
Sujets de recherche
Apprentissage par renforcement
Modèles génératifs
Optimisation
Site web
Google Scholar
LinkedIn

Biographie

Audrey Sedal, professeure adjointe au Département de génie mécanique de l'Université McGill, dirige le groupe MACRObotics (Mechanics, Actuation, Computation for Robotics), où elle se concentre sur la création de techniques et d'outils innovants pour améliorer la sécurité, les capacités et l'intelligence morphologique des robots. Ses recherches actuelles portent notamment sur la conception automatisée et le contrôle de robots souples et extensibles, le développement de matériaux biodégradables pour les robots souples, ainsi que la simulation. La professeure Sedal a obtenu un doctorat en génie mécanique de l'Université du Michigan en 2020. Elle détient également un diplôme de premier cycle en génie mécanique du Massachusetts Institute of Technology (MIT). En plus de ses recherches, Audrey Sedal agit en tant qu'éditrice associée pour l’International Conference on Robotics and Automation (IEEE ICRA) et la conférence Robotics: Science and Systems.

Étudiants actuels

Nathanael Haas
Collaborateur·rice de recherche - IMT Nord Europe
Github
Tanguy Navez
Collaborateur·rice de recherche - Inria Lille
Github
Google Scholar
Shiyao Ni
Maîtrise recherche - McGill
Site web
Github
Google Scholar
Portia Rayner
Maîtrise recherche - McGill

Publications

Acoustic tactile sensing for mobile robot wheels
Wilfred Mason
David Brenken
Falcon Z. Dai
Ricardo Gonzalo Cruz Castillo
Olivier St-Martin Cormier
Audrey Sedal
2024-02-28
ArXiv (prépublication)
doi.org
arxiv.org
Lagrangian Properties and Control of Soft Robots Modeled with Discrete Cosserat Rods
Lekan Molu
Shaoru Chen
Audrey Sedal
The characteristic ``in-plane"bending associated with soft robots' deformation make them preferred over rigid robots in sophisticated manipu… (voir plus)lation and movement tasks. Executing such motion strategies to precision in soft deformable robots and structures is however fraught with modeling and control challenges given their infinite degrees-of-freedom. Imposing \textit{piecewise constant strains} (PCS) across (discretized) Cosserat microsolids on the continuum material however, their dynamics become amenable to tractable mathematical analysis. While this PCS model handles the characteristic difficult-to-model ``in-plane"bending well, its Lagrangian properties are not exploited for control in literature neither is there a rigorous study on the dynamic performance of multisection deformable materials for ``in-plane"bending that guarantees steady-state convergence. In this sentiment, we first establish the PCS model's structural Lagrangian properties. Second, we exploit these for control on various strain goal states. Third, we benchmark our hypotheses against an Octopus-inspired robot arm under different constant tip loads. These induce non-constant ``in-plane"deformation and we regulate strain states throughout the continuum in these configurations. Our numerical results establish convergence to desired equilibrium throughout the continuum in all of our tests. Within the bounds here set, we conjecture that our methods can find wide adoption in the control of cable- and fluid-driven multisection soft robotic arms; and may be extensible to the (learning-based) control of deformable agents employed in simulated, mixed, or augmented reality.
2023-12-10
ArXiv (prépublication)
doi.org
arxiv.org