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  • Faster R-CNN-based Decision Making in a Novel Adaptive Dual-Mode Robotic Anchoring System

Shahin, Shahrooz, Sadeghian, Rasoul and Sareh, Sina ORCID: https://orcid.org/0000-0002-9787-1798, 2021, Conference or Workshop, Faster R-CNN-based Decision Making in a Novel Adaptive Dual-Mode Robotic Anchoring System at 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 30 May- 5 Jun 2021.

Abstract or Description:

This paper proposes a novel adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The module can deploy a suitable mode of attachment, via spines or vacuum suction, to different contact surfaces in response to the textural properties of the surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 100 images of outdoor and indoor surfaces was enhanced using a WGAN-GP generating an additional 200 synthetic images. The enhanced dataset was then used to train a visual surface examination model using Faster R-CNN. The addition of synthetic images increased the mean average precision of the Faster R-CNN model from 81.6% to 93.9%. We have also conducted a series of load tests to characterize the module’s strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of around 22N and 20N when attached using spines and vacuum suction on the ideal surfaces, respectively.

Subjects: Other > Engineering > H600 Electronic and Electrical Engineering > H670 Robotics and Cybernetics > H671 Robotics
School or Centre: Research & Innovation
School of Design
Copyright Holders: Sina Sareh
Funders: EPSRC-UKRI Fellowship (EP/S001840/1).
Date Deposited: 11 Mar 2021 11:59
Last Modified: 11 Mar 2021 12:00
URI: https://researchonline.rca.ac.uk/id/eprint/4751
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