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Arian Sargazi

Doctorat - McGill
Superviseur⋅e principal⋅e
David Meger
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage par renforcement
Robotique
Robotique molle

Publications

Programmable Shape‐Preserving Soft Robotics Arm via Multimodal Multistability
Benyamin Shahryari
Hossein Mofatteh
Arian Sargazi
Armin Mirabolghasemi
David Meger
Abdolhamid Akbarzadeh
Inflatable multistable materials have significantly advanced the design of shape‐preserving soft robotic arms, offering substantial benefi… (voir plus)ts in terms of shape adaptability, energy efficiency, and safety, ensuring operational reliability even in the event of sudden power loss. However, existing strategies for realizing multistable arms often limit themselves to a single mode of multistability, commonly with rotationally symmetric designs favoring extension stability and asymmetric designs inducing bending stability. To address the limitation, this study introduces a pioneering platform termed multimodal multistability that utilizes geometrical frustration. A single cylindrical symmetric cell, designed for extension bistability, could achieve frustrated multistable states in bending by controlling the cell with multiple degrees of freedom incorporated pneumatic actuator. This platform extends the spectrum of attainable stable trajectories while preserving essential attributes of arms, such as load‐bearability, programmability, and reversibility of shape changes. Leveraging a pneumatic system with four degrees of freedom for pressure control, not only enables capturing previously unexplored stable configurations in mechanical metastructures but also allows for the control of their deformation modes. With applications spanning space exploration, medical instruments, and rescue missions, the multimodal multistability promises unparalleled flexibility and efficiency in the design and operation of soft robots.
2024-08-29
Advanced Functional Materials (publié)
doi.org