Underwater Robot-Object Contact Perception using Machine Learning on Force/Torque Sensor Feedback
File(s)
Accepted version
Author(s)
Jamali, Nawid
Kormushev, Petar
Carrera, Arnau
Carreras, Marc
Caldwell, Darwin G
Type
Conference Paper
Abstract
Autonomous manipulation of objects requires re- liable information on robot-object contact state. Underwater environments can adversely affect sensing modalities such as vision, making them unreliable. In this paper we investi- gate underwater robot-object contact perception between an autonomous underwater vehicle and a T-bar valve using a force/torque sensor and the robot’s proprioceptive information. We present an approach in which machine learning is used to learn a classifier for different contact states, namely, a contact aligned with the central axis of the valve, an edge contact and no contact. To distinguish between different contact states, the robot performs an exploratory behavior that produces distinct patterns in the force/torque sensor. The sensor output forms a multidimensional time-series. A probabilistic clustering algo- rithm is used to analyze the time-series. The algorithm dissects the multidimensional time-series into clusters, producing a one- dimensional sequence of symbols. The symbols are used to train a hidden Markov model, which is subsequently used to predict novel contact conditions. We show that the learned classifier can successfully distinguish the three contact states with an accuracy of 72% ± 12 %.
Date Issued
2015-05
Date Acceptance
2015-01-30
Citation
Proc. IEEE Intl Conf. on Robotics and Automation (ICRA 2015), 2015
Publisher
IEEE
Start Page
3915
End Page
3920
Journal / Book Title
Proc. IEEE Intl Conf. on Robotics and Automation (ICRA 2015)
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
ICRA 2015
Start Date
2015-05-26
Finish Date
2015-05-30
Coverage Spatial
Seattle, Washington