Active Multiobject Exploration and Recognition via Tactile Whiskers

Chenxi Xiao, Shujia Xu, Wenzhuo Wu, Juan Wachs

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Robotic exploration under uncertain environments is challenging when optical information is not available. In this article, we propose an autonomous solution of exploring an unknown task space based on tactile sensing alone. We first designed a whisker sensor based on MEMS barometer devices. This sensor can acquire contact information by interacting with the environment nonintrusively. This sensor is accompanied by a planning technique to generate exploration trajectories by using mere tactile perception. This technique relies on a hybrid policy for tactile exploration, which includes a proactive informative path planner for object searching, and a reactive Hopf oscillator for contour tracing. Results indicate that the hybrid exploration policy can increase the efficiency of object discovery. Last, scene understanding was facilitated by segmenting objects and classification. A classifier was developed to recognize the object categories based on the geometric features collected by the whisker sensor. Such an approach demonstrates the whisker sensor, together with the tactile intelligence, can provide sufficiently discriminative features to distinguish objects.

Original languageEnglish
Pages (from-to)3479-3497
Number of pages19
JournalIEEE Transactions on Robotics
Volume38
Issue number6
DOIs
StatePublished - 1 Dec 2022
Externally publishedYes

Keywords

  • Deep learning in robotics and automation
  • force and tactile sensing
  • perception for grasping and manipulation
  • reactive and sensor-based planning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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