Human-Robot Collaboration using Variable Admittance Control and Human Intention Prediction

Admittance control allows a desired dynamic behavior to be reproduced on a non-backdrivable manipulator and it has been widely used for interaction control and, in particular, for human–robot collaboration. Nevertheless, stability problems arise when the environment (e.g. the human) the robot is interacting with becomes too stiff. In this paper, we investigate the stability issues related to a change of stiffness of the human arm during the interaction with an admittance-controlled robot. We propose a novel method for detecting the rise of instability and a passivity-preserving strategy for restoring a stable behavior. The results of the paper are validated on two robotic setups and with 50 users performing two tasks that emulate industrial operations.

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Variable Admittance Control of Robot Manipulators Based on Human Intention

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2019.2910237 ◽

2019 ◽

Vol 24 (3) ◽

pp. 1023-1032 ◽

Cited By ~ 6

Author(s):

Gitae Kang ◽

Hyun Seok Oh ◽

Joon Kyue Seo ◽

Uikyum Kim ◽

Hyouk Ryeol Choi

Keyword(s):

Robot Manipulators ◽

Admittance Control ◽

Human Intention

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A Unified Architecture for Physical and Ergonomic Human–Robot Collaboration

Robotica ◽

10.1017/s026357471900095x ◽

2019 ◽

Vol 38 (4) ◽

pp. 669-683

Author(s):

Federica Ferraguti ◽

Renzo Villa ◽

Chiara Talignani Landi ◽

Andrea Maria Zanchettin ◽

Paolo Rocco ◽

...

Keyword(s):

Industrial Robot ◽

Industrial Applications ◽

Admittance Control ◽

Robotic Cell ◽

Heavy Load ◽

Robotic Assistant ◽

Human Robot Collaboration ◽

Different Parts

SUMMARYIndustrial applications that involve working on and moving a heavy load or that constrain the operator to work in uncomfortable positions can take advantage of the assistance of a robotic assistant. In this paper, we propose an architecture for an ergonomic human–robot co-manipulation of objects of various shapes and weight. The object is carried by the robot and, thanks to an ergonomic planner, is positioned in the most comfortable way for the user. Furthermore, thanks to an admittance control with payload compensation, the user can easily adjust the position of the object for working on different parts of it. The proposed architecture is experimentally validated in a robotic cell including an ABB industrial robot.

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A Conceptual Approach of Passive Human-Intention-Orientated Variable Admittance Control using Power Envelope

10.1109/iros51168.2021.9636487 ◽

2021 ◽

Author(s):

Jingdong Chen ◽

Paul I. Ro

Keyword(s):

Admittance Control ◽

Conceptual Approach ◽

Human Intention

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Human Intention Inference and On-Line Human Hand Motion Prediction for Human-Robot Collaboration

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros40897.2019.8968192 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ren.C Luo ◽

Licong Mai

Keyword(s):

Motion Prediction ◽

Human Hand ◽

Hand Motion ◽

Human Intention ◽

On Line ◽

Human Robot Collaboration ◽

Intention Inference

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Variable Admittance Control for Human-Robot Collaboration in Robot-Assisted Orthopedic Surgery*

2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) ◽

10.1109/robio49542.2019.8961583 ◽

2019 ◽

Author(s):

Jiawei Liang ◽

Yuanyuan Yang ◽

Yu Wang ◽

Shoubin Liu ◽

Mu Lu ◽

...

Keyword(s):

Orthopedic Surgery ◽

Admittance Control ◽

Robot Assisted ◽

Human Robot Collaboration

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Human Intention Estimation With Tactile Sensors in Human-Robot Collaboration

Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications ◽

10.1115/dscc2017-5291 ◽

2017 ◽

Cited By ~ 2

Author(s):

Yiwei Wang ◽

Yixuan Sheng ◽

Ji Wang ◽

Wenlong Zhang

Keyword(s):

Machine Learning ◽

Real Time ◽

Machine Learning Algorithms ◽

Force Distribution ◽

Paper Machine ◽

Tactile Sensors ◽

Human Intention ◽

Interaction Surface ◽

Human Robot Collaboration ◽

Intention Estimation

In this paper, machine learning methods are proposed for human intention estimation based on the change of force distribution on the interaction surface during human-robot collaboration (HRC). The force distribution under different human intentions are examined when the human and robot are jointly carrying the same piece of object. A pair of Robotiq tactile sensors is applied to monitor the change of force distribution on the interaction surface. Three machine learning algorithms are tested on recognition of human intentions based on the force distribution patterns on the contact surface of grippers for the manipulator. The K-nearest Neighbor model is selected to build a real-time framework, which includes human intention estimation and cooperative motion planning for the robot manipulator. A real-time experiment is conducted to validate the method, which suggests the human intention estimation approach can help enhance the efficiency of HRC.

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