Magnetic and Mechanical Modeling of a Soft Three-Axis Force Sensor

Chathuranga, Damith Suresh; Wang, Zhongkui; Noh, Yohan; Nanayakkara, Thrishantha; Hirai, Shinichi

doi:10.1109/jsen.2016.2550605

Cited by 42 publications

(17 citation statements)

References 48 publications

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“…A conventional optical flexure sensor has been implemented in a tendon‐driven compliant gripper . Embedding small magnets in an elastomer a few mm above a two or three axis Hall sensor or matrix of Hall sensors allows the measurement of normal and shear force, allowing both tactile sensing as well as use in closed‐loop control. The method is fast, and has low hysteresis.…”

Section: Sensing Technologiesmentioning

confidence: 99%

Soft Robotic Grippers

et al. 2018

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Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

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Section: Sensing Technologiesmentioning

confidence: 99%

Soft Robotic Grippers

et al. 2018

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“…Among these sensors, a pyramid-shaped tactile sensor embedded with a 3-axis Hall element was proposed to estimate normal and shear forces with high accuracy [7]. A mathematical model of 3-axis force approximation based on a cylindrical-shaped tactile sensor was presented in [8]. The application of Hall-effect sensors was further investigated in the development of a distributed tactile sensor for measuring 3-axis forces [9].…”

Section: Related Workmentioning

confidence: 99%

Geometry Optimisation of a Hall-Effect-Based Soft Fingertip for Estimating Orientation of Thin Rectangular Objects

Rosle

Wang

Hirai

2019

Sensors

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Soft tactile sensors have been applied to robotic grippers for assembly. It is a challenging task to obtain contact information and object orientation using tactile sensors during grasping. Currently, the design of Hall-effect-based tactile sensors to perform such tasks is based on trial and error. We present a method of investigating the optimal geometrical design of a cylindrical soft sensor to increase its sensitivity. The finite element model of a soft fingertip was constructed in Abaqus with two design variables, i.e., hollow radius and magnet position. Then, the model was imported into Isight, with the maximisation of magnet displacement as the objective function. We found that the optimal design was at the boundary of the parameter design space. Four fingertips were fabricated with one intuitive, one optimal, and two optional sets of parameters. Experiments were performed, and object orientation was estimated by utilising linear approximation and a machine learning approach. Good agreements were achieved between optimisation and experiments. The results revealed that the estimated average error in object orientation was decreased by the optimised fingertip design. Furthermore, the 3-axis forces could successfully be estimated based on sensor outputs.

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“…This idea has since been employed in designs using various transduction methods. Tactile sensors that rely on the compliance of an intermediary medium have been developed using various sensing methods such as magnetic [65][66][67][68], ultrasonic [69], capacitive [11], fluidic [70][71][72], piezoelectric and piezoresistive [73] and optical [74,75] methods. These all endeavour to measure applied forces by measuring the deformation of the compliant medium, similarly to Clark's initial concept.…”

Section: Compliance-based Designsmentioning

confidence: 99%