TY - JOUR
T1 - On an Improved State Parametrization for Soft Robots with Piecewise Constant Curvature and Its Use in Model Based Control
AU - Della Santina, Cosimo
AU - Bicchi, Antonio
AU - Rus, Daniela
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Piecewise constant curvature models have proven to be an useful tool for describing kinematics and dynamics of soft robots. However, in their three dimensional formulation they suffer from many issues limiting their range of applicability - as discontinuities and singularities - mainly concerning the straight configuration of the robot. In this work we analyze these flaws, and we show that they are not due to the piecewise constant curvature assumption itself, but that instead they are a byproduct of the commonly employed direction/angle of bending parametrization of the state. We therefore consider an alternative state representation which solves all the discussed issues, and we derive a model based controller based on it. Examples in simulation are provided to support and describe the theoretical results. When using the novel parametrization, the system is able to perform more complex tasks, with a strongly reduced computational burden, and without incurring in spikes and discontinuous behaviors.
AB - Piecewise constant curvature models have proven to be an useful tool for describing kinematics and dynamics of soft robots. However, in their three dimensional formulation they suffer from many issues limiting their range of applicability - as discontinuities and singularities - mainly concerning the straight configuration of the robot. In this work we analyze these flaws, and we show that they are not due to the piecewise constant curvature assumption itself, but that instead they are a byproduct of the commonly employed direction/angle of bending parametrization of the state. We therefore consider an alternative state representation which solves all the discussed issues, and we derive a model based controller based on it. Examples in simulation are provided to support and describe the theoretical results. When using the novel parametrization, the system is able to perform more complex tasks, with a strongly reduced computational burden, and without incurring in spikes and discontinuous behaviors.
KW - Modeling
KW - and learning for soft robots
KW - control
KW - motion control
KW - natural machine motion
UR - http://www.scopus.com/inward/record.url?scp=85079640563&partnerID=8YFLogxK
U2 - 10.1109/LRA.2020.2967269
DO - 10.1109/LRA.2020.2967269
M3 - Article
AN - SCOPUS:85079640563
SN - 2377-3766
VL - 5
SP - 1001
EP - 1008
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 2
M1 - 8961972
ER -