Soft and Highly-Integrated Optical Fiber Bending Sensors for Proprioception in Multi-Material 3D Printed Fingers

TL;DR

This paper introduces a rapid, semi-automated 3D printing method to embed optical fiber sensors in soft robotic fingers, enabling accurate shape sensing and contact detection with minimal manual assembly.

Contribution

A novel semi-automated printing process for integrating optical fibers into soft robot fingers, improving ease of manufacturing and sensor performance.

Findings

Sensors achieve 70% linearity and 4.81° RMS error.

Fingertip position can be estimated within 12 mm accuracy.

Distributed sensors enable contact detection independent of actuation feedback.

Abstract

Accurate shape sensing, only achievable through distributed proprioception, is a key requirement for closed-loop control of soft robots. Low-cost power efficient optoelectronic sensors manufactured from flexible materials represent a natural choice as they can cope with the large deformations of soft robots without loss of performance. However, existing integration approaches are cumbersome and require manual steps and complex assembly. We propose a semi-automated printing process where plastic optical fibers are embedded with readout electronics in 3D printed flexures. The fibers become locked in place and the readout electronics remain optically coupled to them while the flexures undergo large bending deformations, creating a repeatable, monolithically manufactured bending transducer with only 10 minutes required in total for the manual embedding steps. We demonstrate the process by manufacturing multi-material 3D printed fingers and extensively evaluating the performance of each proprioceptive joint. The sensors achieve 70% linearity and 4.81{\deg} RMS error on average. Furthermore, the distributed architecture allows for maintaining an average fingertip position estimation accuracy of 12 mm in the presence of external static forces. To demonstrate the potential of the distributed sensor architecture in robotics applications, we build a data-driven model independent of actuation feedback to detect contact with objects in the environment.