Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...

Design Example: Resistive Touchscreen

Design Example: Resistive Touchscreen

A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...

Drag

Drag

Drag is a resistive force opposing an object’s motion through a fluid, resulting from surface pressure and shear forces. It comprises two components: a perpendicular one from pressure and a tangential one from shear stress. Accurate drag calculations use pressure and wall shear stress distributions, often determined through Computational Fluid Dynamics (CFD) or wind tunnel testing. The drag coefficient, a dimensionless measure, depends on factors like shape, Reynolds number, Mach number, Froude...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Wireless-Powered Flexible Bioelectronic Implants with Deformation-Aware Optimization.

IEEE transactions on bio-medical engineering·2026

Same author

Multimodal feedback enhances human belief updating performance and reduces cognitive load in urban drone operation.

Frontiers in robotics and AI·2026

Same author

Blood pressure-stratified associations of the atherogenic index of plasma with all-cause mortality: a 10-year rural cohort study in China.

Frontiers in cardiovascular medicine·2026

Same author

Spatiotemporal neural dynamics of Chinese word form processing: An SEEG study.

Cognitive neuropsychology·2026

Same author

Reaction-Kinetics-Driven Epitaxy of Wafer-Scale WS<sub>2</sub> by Molten Precursor Engineering.

Nano letters·2026

Same author

Multi-targeted protection of Yuping Tongqiao against allergic rhinitis: suppression of inflammatory response via TSLP signaling and reinforcement of epithelial barrier integrity via AhR signaling.

Frontiers in allergy·2026

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

A Soft Collaborative Robot for Contact-based Intuitive Human Drag Teaching.

Shoulu Gong¹, Wenbo Li^2,3, Jiahao Wu¹

¹University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|April 22, 2024

Summary

This summary is machine-generated.

This study introduces a soft collaborative robot (Soft Co-bot) with easy drag-teaching programming and high precision. These robots overcome limitations of soft materials for safer, intuitive human-robot collaboration in industrial settings.

Keywords:

drag teaching exceptional repeatability human collaboration soft robotics ultra‐low hysteresis

More Related Videos

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Related Experiment Videos

Last Updated: Jun 9, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics
Materials Science
Human-Robot Interaction

Background:

Soft material robots offer safety and compliance for human-robot collaboration.
Current soft robots face challenges in programmability and motion precision due to material properties like super-elasticity and hysteresis.

Purpose of the Study:

To develop a soft collaborative robot (Soft Co-bot) with intuitive programming and enhanced motion precision.
To enable practical applications of soft robots in industrial settings through improved control and collaboration.

Main Methods:

A biomimetic antagonistic design was implemented in a pneumatic soft robot.
A self-sensing system was created using cables, servo motors, and tension sensors for precise actuation and drag-aware collaboration.

Main Results:

The Soft Co-bot demonstrates intuitive programming via contact-based drag teaching.
Exceptional motion repeatability (< 0.30% of body length) and ultra-low hysteresis (< 2.0%) were achieved.
The robot can be taught tasks by human drag and then precisely repeat them autonomously.

Conclusions:

The developed Soft Co-bots offer a solution to the programmability and precision limitations of soft robots.
These robots have high potential for safe, intuitive human-robot collaboration in unstructured environments.
The findings promote the immediate practical application of soft robots in industries like electronics assembly and machine tool installation.