Soft robotic grippers are becoming increasingly popular for agricultural and logistics automation. Their passive conformability enables them to adapt to varying product shapes and sizes, providing stable large-area grasps. This work presents a novel methodology for combining soft robotic grippers with electrical impedance tomography-based sensors to infer intrinsic properties of grasped fruits. We use a Fin Ray soft robotic finger with embedded microspines to grab and obtain rich multi-direction electrical properties of the object. Learning-based techniques are then used to infer the desired fruit properties. The framework is extensively tested and validated on multiple fruit groups. Our results show that ripeness parameters and even weight of the grasped fruit can be estimated with reasonable accuracy autonomously using the proposed system.
Automated Fruit Quality Testing using an Electrical Impedance Tomography-Enabled Soft Robotic Gripper
2022-12-26
In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (pp. pp. 8500-8506). IEEE: Kyoto, Japan. (2022)
Paper
Electronic Resource
English
Automation , Shape , Fingers , Soft robotics , Sensors , Impedance , Grippers
DDC: | 629 |
NTRS | 1991
|Rolling-Friction Robotic Gripper
NTRS | 1992
|Tactile perception in hydrogel-based robotic skins using data-driven electrical impedance tomography
BASE | 2023
|