Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization
This work addresses the problem of accurate 3D shape sensing for soft continuum arms, which is crucial for their autonomous operation.
This paper proposes a vision-based shape estimator for soft continuum arms that uses a geometric strain parametrization to reduce the dimensionality of the shape. The method achieves end-effector accuracy less than the soft arm's radius.
Interest in soft continuum arms has increased as their inherent material elasticity enables safe and adaptive interactions with the environment. However to achieve full autonomy in these arms, accurate three-dimensional shape sensing is needed. Vision-based solutions have been found to be effective in estimating the shape of soft continuum arms. In this paper, a vision-based shape estimator that utilizes a geometric strain based representation for the soft continuum arm's shape, is proposed. This representation reduces the dimension of the curved shape to a finite set of strain basis functions, thereby allowing for efficient optimization for the shape that best fits the observed image. Experimental results demonstrate the effectiveness of the proposed approach in estimating the end effector with accuracy less than the soft arm's radius. Multiple basis functions are also analyzed and compared for the specific soft continuum arm in use.