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AmbiSense: Acoustic Field Based Blindspot-Free Proximity Detection and Bearing Estimation

Siddharth Rupavatharam, Xiaoran Fan, Caleb Escobedo, Daewon Lee, Larry Jackel, Richard Howard, Colin Prepscius, Daniel Lee, Volkan Isler

Year
2023
Citations
7

Abstract

In this paper, we present AmbiSense, an acoustic field based sensing system that performs proximity detection and bearing estimation for safer physical human-robot interactions. A single low cost piezoelectric transducer is used to setup this novel acoustic sensing modality to create a blindspot-free sound field engulfing a robot arm. Two detection algorithms leveraging spectral information from reflected audio waves of objects entering the acoustic field are proposed to infer object presence and bearing. We also present a new receiver structure which improves signal to noise ratio (SNR). AmbiSense is paired with a collision avoidance inverse kinematic solver for real world deployment on a Kinova Gen3 robot. Validation is performed using ten test objects generating 2000 proximity and bearing estimation events in real world settings, we show that AmbiSense detects proximity with 93.8% sensitivity and 96.6 % specificity. It estimates bearing and maps it to three zones on a robot link with 100% sensitivity and specificity, while using fewer sensors than state of the art methods for similar coverage.

Keywords

Bearing (navigation)RobotObject detectionComputer scienceSensitivity (control systems)Artificial intelligenceAcousticsFree fieldComputer visionPattern recognition (psychology)

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