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

2025-02-06

AI Summary

To address the challenges of shape sensing and closed-loop control in soft robots, this work proposes a low-cost, easily integrable embedded optical fiber bending sensor. The method introduces a novel semi-automated, monolithic multi-material 3D printing process that simultaneously embeds plastic optical fiber (POF) and readout electronics into a flexible bending structure, enabling single-chip fabrication and optically stable coupling under large deformations. It integrates POF-based strain sensing, embedded optoelectronic integration, and data-driven model-free contact detection. Experimental results demonstrate a linearity of 70%, a mean bending angle estimation error of 4.81°, fingertip position estimation accuracy of 12 mm, and robust object contact recognition under external force disturbances. This work presents the first fully integrated, distributed proprioceptive sensing solution specifically designed for soft actuators.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Distributed proprioception for soft robots
• Low-cost optical fiber bending sensors
• Semi-automated 3D printing integration process

Innovation

• Semi-automated printing process
• Plastic optical fibers embedded
• Multi-material 3D printed fingers