Sensing soft robots' shape with cameras

an investigation on kinematics-aware SLAM

Conference paper (2022)

Authors

Emanuele Riccardo Rosi University of Genoa
Maximilian Stölzle Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Fabio Solari University of Genoa
C. Della Santina German Aerospace Center, Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Human-Robot Interaction (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Emanuele Riccardo Rosi, Maximilian Stölzle, Fabio Solari, C. Della Santina
DOI: https://doi.org/10.1109/RoboSoft54090.2022.9762199
Estimation Kinematics Shape Soft robotics Robot kinematics Robot vision systems Simultaneous localization and mapping
More Info
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Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Human-Robot Interaction

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.