Sensing soft robots' shape with cameras: an investigation on kinematics-aware SLAM

Abstract

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.

Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors

“…Several sensing modalities have been considered to implement shape sensing, such as resistive, 8,9 capacitive, 10,11 optical, 12 and visual. 13 Magnetic sensors 14–18 are a promising solution as they are compact, highly sensitive, and can be easily integrated into existing soft robot designs. Thus, they can provide reliable and fully proprioceptive measurements at the cost of a minimal decrease in the robot's softness.…”