Design of a Multimodal Fingertip Sensor for Dynamic Manipulation

TL;DR

This paper presents a novel spherical multimodal fingertip sensor combining pressure and proximity sensors, utilizing neural networks for contact estimation, and demonstrates its effectiveness in dynamic manipulation tasks.

Contribution

It introduces a compact, low-latency sensor with integrated neural network-based contact estimation for improved dynamic manipulation capabilities.

Findings

Sensor achieves up to 200 Hz sampling rate.

Effective in contact transition detection and collision avoidance.

Quantified impact of latency on manipulation performance.

Abstract

We introduce a spherical fingertip sensor for dynamic manipulation. It is based on barometric pressure and time-of-flight proximity sensors and is low-latency, compact, and physically robust. The sensor uses a trained neural network to estimate the contact location and three-axis contact forces based on data from the pressure sensors, which are embedded within the sensor's sphere of polyurethane rubber. The time-of-flight sensors face in three different outward directions, and an integrated microcontroller samples each of the individual sensors at up to 200 Hz. To quantify the effect of system latency on dynamic manipulation performance, we develop and analyze a metric called the collision impulse ratio and characterize the end-to-end latency of our new sensor. We also present experimental demonstrations with the sensor, including measuring contact transitions, performing coarse mapping, maintaining a contact force with a moving object, and reacting to avoid collisions.