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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

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

Muhammad Hisyam Rosle¹, Zhongkui Wang², Shinichi Hirai³

¹Department of Robotics, Graduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan. rr0000xe@ed.ritsumei.ac.jp.

Sensors (Basel, Switzerland)

|September 25, 2019

Summary

Optimizing soft tactile sensor geometry enhances robotic grasping by improving object orientation estimation and 3-axis force sensing. This reduces errors in robotic assembly tasks.

Keywords:

force sensing optimisation soft robotics tactile sensor

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Optimization, Test and Diagnostics of Miniaturized Hall Thrusters

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Published on: February 16, 2019

Area of Science:

Robotics
Materials Science
Sensor Technology

Background:

Soft tactile sensors are crucial for robotic grippers in assembly tasks.
Current Hall-effect sensor design relies on trial-and-error, limiting performance.
Accurate contact information and object orientation are challenging to achieve during grasping.

Purpose of the Study:

To investigate optimal geometrical designs for cylindrical soft tactile sensors.
To enhance sensor sensitivity for improved robotic grasping capabilities.
To reduce errors in object orientation estimation during robotic manipulation.

Main Methods:

Finite element modeling (FEM) of a soft fingertip in Abaqus.
Optimization using Isight, maximizing magnet displacement as the objective function.
Fabrication and experimental validation of optimized and non-optimized sensor designs.

Main Results:

Optimal sensor design identified at the boundary of the parameter space.
Experimental validation showed reduced average error in object orientation estimation.
Successful estimation of 3-axis forces using the optimized sensor outputs.

Conclusions:

The proposed method effectively optimizes soft tactile sensor geometry for enhanced performance.
Optimized sensors significantly improve object orientation estimation accuracy in robotic grasping.
The developed sensors show potential for precise force sensing in assembly applications.