Compact LED-Based Displacement Sensing for Robot Fingers

2024-10-04

AI Summary

Compact, robust displacement sensing is critical for robotic fingers but remains challenging due to size, power, and integration constraints. Method: This work introduces a passive, LED-based self-emissive–self-receptive displacement sensor that exploits force-induced deformation of a transparent elastomer to modulate LED light intensity—enabling micrometer-scale displacement measurement without external illumination or amplification circuitry. A single LED serves dual roles (emitter and photodetector), supported by coupled mechanical–optical modeling and supervised learning for 3-axis force/torque inversion. Contribution/Results: The finger-sized sensor exhibits high overload tolerance against shear and bending moments, enables low-cost mass fabrication, and facilitates rapid integration into dexterous hands. Experimental evaluation demonstrates high reliability and applicability in manipulation tasks—including grasping and sliding—with root-mean-square errors of 0.05–0.07 N across all three force components.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we introduce a sensor designed for integration in robot fingers, where it can provide information on the displacements induced by external contact. Our sensor uses LEDs to sense the displacement between two plates connected by a transparent elastomer; when a force is applied to the finger, the elastomer displaces and the LED signals change. We show that using LEDs as both light emitters an receivers in this context provides high sensitivity, allowing such an emitter and receiver pairs to detect very small displacements. We characterize the standalone performance of the sensor by testing the ability of a supervised learning model to predict complete force and torque data from its raw signals, and obtain a mean error between 0.05 and 0.07 N across the three directions of force applied to the finger. Our method allows for finger-size packaging with no amplification electronics, low cost manufacturing, easy integration into a complete hand, and high overload shear forces and bending torques, suggesting future applicability to complete manipulation tasks.

Problem

• Develops a compact LED-based displacement sensor for robot fingers.
• Detects small displacements using LED emitter-receiver pairs.
• Enables force and torque prediction with low error rates.

Innovation

• LEDs sense displacement via transparent elastomer
• Supervised learning predicts force and torque
• Compact, low-cost, high-sensitivity finger sensor