Acoustic collision detection and localization for robot manipulators [conference paper]

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - October 25-29, 2020

Authors

Xiaoran Fan, Daewon Lee, Yuan Chen, Colin Prepscius, Volkan Isler (professor), Larry Jackel, H Sebastian Seung, Daniel Lee

Abstract

Collision detection is critical for safe robot operation in the presence of humans. Acoustic information originating from collisions between robots and objects provides opportunities for fast collision detection and localization; however, audio information from microphones on robot manipulators needs to be robustly differentiated from motors and external noise sources. In this paper, we present Panotti, the first system to efficiently detect and localize on-robot collisions using low-cost microphones. We present a novel algorithm that can localize the source of a collision with centimeter level accuracy and is also able to reject false detections using a robust spectral filtering scheme. Our method is scalable, easy to deploy, and enables safe and efficient control for robot manipulator applications. We implement and demonstrate a prototype that consists of 8 miniature microphones on a 7 degree of freedom (DOF) manipulator to validate our design. Extensive experiments show that Panotti realizes near perfect on-robot true positive collision detection rate with almost zero false detections even in high noise environments. In terms of accuracy, it achieves an average localization error of less than 3.8 cm under various experimental settings.

Link to full paper

Acoustic collision detection and localization for robot manipulators

Keywords

robotics