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Title: Contact detection and object size estimation using a modular soft gripper with embedded flex sensors
Authors: Elgeneidy, Khaled
Neumann, Gerhard
Pearson, Simon
Jackson, Michael R.
Lohse, Niels
Issue Date: 2018
Publisher: © IEEE
Citation: ELGENEIDY, K. ... et al., 2018. Contact detection and object size estimation using a modular soft gripper with embedded flex sensors. Presented at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, Oct 1-5th, pp.498-503.
Abstract: Soft-grippers can grasp delicate and deformable objects without bruise or damage as the gripper can adapt to the object’s shape. However, the contact forces are still hard to regulate due to missing contact feedback of such grippers. In this paper, a modular soft gripper design is presented utilizing interchangeable soft pneumatic actuators with embedded flex sensors as fingers of the gripper. The fingers can be assembled in different configurations using 3D printed connectors. The paper investigates the potential of utilizing the simple sensory feedback from the flex sensors to make additional meaningful inferences regarding the contact state and grasped object size. We study the effect of the grasped object size and contact type on the combined feedback from the embedded flex sensors of all fingers. Our results show that a simple linear relationship exists between the grasped object size and the final flex sensor reading at fixed input conditions, despite the variation in object weight and contact type. Additionally, by simply monitoring the time series response from the flex sensor, contact can be detected by comparing the response to the known free-bending response at the same input conditions. Furthermore, by utilizing the measured internal pressure supplied to the soft fingers, it is possible to distinguish between power and pinch grasps, as the nature of the contact affects the rate of change in the flex sensor readings against the internal pressure.
Description: © 2018 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.
Sponsor: The reported work has been partially funded by the EPSRC Centre for Innovated Manufacturing in Intelligent Automation (EP/IO33467/1).
Version: Accepted for publication
DOI: 10.1109/IROS.2018.8593399
URI: https://dspace.lboro.ac.uk/2134/34063
Publisher Link: https://doi.org/10.1109/IROS.2018.8593399
ISBN: 9781538680940
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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