Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots.

ABSTRACT: Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time, which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft

SUBMITTER: Culha U 

PROVIDER: S-EPMC7610446 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature