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Design, Implementation, and Observer-based Output Control of a Super-coiled Polymer-Driven Two Degree-of-Freedom Robotic Eye.


ABSTRACT: The prevalence of ineffective corrective surgeries for ocular motor disorders calls for a robotic eye platform in aiding ophthalmologists to better understand the biomechanisms of human eye movement. This letter presents the first hardware design and implementation of a 2-DOF robotic eye driven by super-coiled polymer (SCP) artificial muscles. While our previous work designed and simulated a deep deterministic policy gradient (DDPG) learning-based controller that requires full-state feedback of the SCP-driven robotic eye, measuring the temperature states of the slender SCPs is generally impractical for the ubiquitously aimed robot. To address this predicament, this letter proposes a reduced-order state observer to estimate the temperature of SCPs given the kinematic measurements. Combining the designed observer and the learning-based controller, the closed-loop output feedback control is implemented on the robotic eye prototype to examine its performance on three classical types of eye movements: visual fixation, saccadic pursuit, and smooth pursuit. The experimental results are presented which successfully validate the observer-based output control of the SCP-driven robotic eye.

SUBMITTER: Rajendran SK 

PROVIDER: S-EPMC10593108 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Design, Implementation, and Observer-based Output Control of a Super-coiled Polymer-Driven Two Degree-of-Freedom Robotic Eye.

Rajendran Sunil Kumar SK   Wei Qi Q   Yao Ningshi N   Zhang Feitian F  

IEEE robotics and automation letters 20230802 9


The prevalence of ineffective corrective surgeries for ocular motor disorders calls for a robotic eye platform in aiding ophthalmologists to better understand the biomechanisms of human eye movement. This letter presents the first hardware design and implementation of a 2-DOF robotic eye driven by super-coiled polymer (SCP) artificial muscles. While our previous work designed and simulated a deep deterministic policy gradient (DDPG) learning-based controller that requires full-state feedback of  ...[more]

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