TY - GEN
T1 - Sensing soft robots' shape with cameras
T2 - 5th IEEE International Conference on Soft Robotics, RoboSoft 2022
AU - Rosi, Emanuele Riccardo
AU - Stolzle, Maximilian
AU - Solari, Fabio
AU - Santina, Cosimo Della
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The nature of continuum soft robots calls for novel perception solutions, which can provide information on the robot's shape while not substantially modifying their bodies' softness. One way to achieve this goal is to develop innovative and completely deformable sensors. However, these solutions tend to be less reliable than classic sensors for rigid robots. As an alternative, we consider here the use of monocular cameras. By admitting a small rigid component in our design, we can leverage well-established solutions from mobile robotics. We propose a shape sensing strategy that combines a SLAM algorithm with nonlinear optimization based on the robot's kinematic model. We prove the method's effectiveness in simulation and with experiments of a single-segment continuous soft robot with a camera mounted to the tip. We achieve mean relative translational errors below 9% simulations and experiments alike, and as low as 0.5% on average for some simulation conditions.
AB - The nature of continuum soft robots calls for novel perception solutions, which can provide information on the robot's shape while not substantially modifying their bodies' softness. One way to achieve this goal is to develop innovative and completely deformable sensors. However, these solutions tend to be less reliable than classic sensors for rigid robots. As an alternative, we consider here the use of monocular cameras. By admitting a small rigid component in our design, we can leverage well-established solutions from mobile robotics. We propose a shape sensing strategy that combines a SLAM algorithm with nonlinear optimization based on the robot's kinematic model. We prove the method's effectiveness in simulation and with experiments of a single-segment continuous soft robot with a camera mounted to the tip. We achieve mean relative translational errors below 9% simulations and experiments alike, and as low as 0.5% on average for some simulation conditions.
UR - https://www.scopus.com/pages/publications/85129968300
U2 - 10.1109/RoboSoft54090.2022.9762199
DO - 10.1109/RoboSoft54090.2022.9762199
M3 - Conference contribution
AN - SCOPUS:85129968300
T3 - 2022 IEEE 5th International Conference on Soft Robotics, RoboSoft 2022
SP - 795
EP - 801
BT - 2022 IEEE 5th International Conference on Soft Robotics, RoboSoft 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 April 2022 through 8 April 2022
ER -