A three-dimensional force estimation method for the cable-driven soft robot based on monocular images

TL;DR

This paper introduces an end-to-end neural network that estimates 3D contact forces in cable-driven soft robots using monocular images and actuation data, enabling real-time, accurate force feedback for safer and more precise manipulation.

Contribution

It presents a novel approach that fuses monocular images with actuation signals for 3D force estimation, reducing preprocessing complexity and improving accuracy over existing methods.

Findings

Achieved a mean relative error of 0.84% in 3D force estimation.

Validated the method on a soft robot testbed with promising results.

Outperformed previous methods with a lower error rate.

Abstract

Soft manipulators are known for their superiority in coping with high-safety-demanding interaction tasks, e.g., robot-assisted surgeries, elderly caring, etc. Yet the challenges residing in real-time contact feedback have hindered further applications in precise manipulation. This paper proposes an end-to-end network to estimate the 3D contact force of the soft robot, with the aim of enhancing its capabilities in interactive tasks. The presented method features directly utilizing monocular images fused with multidimensional actuation information as the network inputs. This approach simplifies the preprocessing of raw data compared to related studies that utilize 3D shape information for network inputs, consequently reducing configuration reconstruction errors. The unified feature representation module is devised to elevate low-dimensional features from the system's actuation signals to the same level as image features, facilitating smoother integration of multimodal information. The proposed method has been experimentally validated in the soft robot testbed, achieving satisfying accuracy in 3D force estimation (with a mean relative error of 0.84% compared to the best-reported result of 2.2% in the related works).