Variable admittance control preventing undesired oscillating behaviors in physical human-robot interaction

2017 ◽  
Author(s):  
Chiara Talignani Landi ◽  
Federica Ferraguti ◽  
Lorenzo Sabattini ◽  
Cristian Secchi ◽  
Marcello Bonfe ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction

scholarly journals Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

2021 ◽  
pp. 1-1
Author(s):  
Yahya M. Hamad ◽  
Yusuf Aydin ◽  
Cagatay Basdogan
Keyword(s):  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction ◽  
Force Scaling

Cartesian Admittance Control Based on Maxwell Model for Human Robot Interaction

2021 ◽  
pp. 451-458
Author(s):  
Xin Wang ◽  
Jia Sun ◽  
Zhijun Gao ◽  
Languang Zhao ◽  
Jianshun Liu ◽  
...  
Keyword(s):  
Maxwell Model ◽  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction

scholarly journals A variable admittance control strategy for stable physical human–robot interaction

2019 ◽  
Vol 38 (6) ◽  
pp. 747-765 ◽  
Author(s):  
Federica Ferraguti ◽  
Chiara Talignani Landi ◽  
Lorenzo Sabattini ◽  
Marcello Bonfè ◽  
Cesare Fantuzzi ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction ◽  
Interaction Control ◽  
Human Arm ◽  
Industrial Operations ◽  
Novel Method ◽  
The Stability ◽  
Stability Issues ◽  
Human Robot Collaboration

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.

scholarly journals Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141877319 ◽  
Author(s):  
S M Mizanoor Rahman ◽  
Ryojun Ikeura
Keyword(s):  
Predictive Control ◽  
Optimization Algorithm ◽  
Weight Perception ◽  
Robotic System ◽  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction ◽  
Heavy Object ◽  
Control Scheme ◽  
And Performance

In the first step, a one degree of freedom power assist robotic system is developed for lifting lightweight objects. Dynamics for human–robot co-manipulation is derived that includes human cognition, for example, weight perception. A novel admittance control scheme is derived using the weight perception–based dynamics. Human subjects lift a small-sized, lightweight object with the power assist robotic system. Human–robot interaction and system characteristics are analyzed. A comprehensive scheme is developed to evaluate the human–robot interaction and performance, and a constrained optimization algorithm is developed to determine the optimum human–robot interaction and performance. The results show that the inclusion of weight perception in the control helps achieve optimum human–robot interaction and performance for a set of hard constraints. In the second step, the same optimization algorithm and control scheme are used for lifting a heavy object with a multi-degree of freedom power assist robotic system. The results show that the human–robot interaction and performance for lifting the heavy object are not as good as that for lifting the lightweight object. Then, weight perception–based intelligent controls in the forms of model predictive control and vision-based variable admittance control are applied for lifting the heavy object. The results show that the intelligent controls enhance human–robot interaction and performance, help achieve optimum human–robot interaction and performance for a set of soft constraints, and produce similar human–robot interaction and performance as obtained for lifting the lightweight object. The human–robot interaction and performance for lifting the heavy object with power assist are treated as intuitive and natural because these are calibrated with those for lifting the lightweight object. The results also show that the variable admittance control outperforms the model predictive control. We also propose a method to adjust the variable admittance control for three degrees of freedom translational manipulation of heavy objects based on human intent recognition. The results are useful for developing controls of human friendly, high performance power assist robotic systems for heavy object manipulation in industries.

scholarly journals Admittance control parameter adaptation for physical human-robot interaction

2017 ◽  
Author(s):  
Chiara Talignani Landi ◽  
Federica Ferraguti ◽  
Lorenzo Sabattini ◽  
Cristian Secchi ◽  
Cesare Fantuzzi
Keyword(s):  
Control Parameter ◽  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction ◽  
Parameter Adaptation ◽  
Control Parameter Adaptation

Unified Robot Control Scheme for Cooperative Motion, Autonomous Motion and Contact Reaction

2011 ◽  
Vol 23 (4) ◽  
pp. 557-566 ◽  
Author(s):  
Vincent Duchaine ◽  
◽  
Clément Gosselin ◽  
Keyword(s):  
Robot Control ◽  
Human Robot Interaction ◽  
Future Generations ◽  
Admittance Control ◽  
Robot Interaction ◽  
Modes Of Operation ◽  
Cooperative Motion ◽  
Control Scheme ◽  
Industrial Manipulators ◽  
Autonomous Motion

While the majority of industrial manipulators currently in use only need to performautonomousmotion, future generations of cooperative robots will also have to execute cooperative motion and intelligently react to contacts. These extended behaviours are essential to enable safe and effective physical Human-Robot Interaction (pHRI). However, they will inevitably result in an increase of the controller complexity. This paper presents a single variable admittance control scheme that handles the three modes of operation, thereby minimizing the complexity of the controller. First, the adaptative admittance controller previously proposed by the authors for cooperative motion is recalled. Then, a novel implementation of variable admittance control for the generation of smooth autonomous motion including reaction to collisions anywhere on the robot is presented. Finally, it is shown how the control equations for these three modes of operation can be simply unified into a unique control scheme.

Intent aware adaptive admittance control for physical Human-Robot Interaction

2015 ◽  
Author(s):  
Isura Ranatunga ◽  
Sven Cremer ◽  
Dan O. Popa ◽  
Frank L. Lewis
Keyword(s):  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction

Unified Virtual Guides Framework for Path Tracking Tasks

Robotica ◽  
2019 ◽  
Vol 38 (10) ◽  
pp. 1807-1823 ◽  
Author(s):  
Leon Žlajpah ◽  
Tadej Petrič
Keyword(s):  
Control Algorithm ◽  
Degrees Of Freedom ◽  
Path Tracking ◽  
Human Robot Interaction ◽  
Task Space ◽  
Admittance Control ◽  
Unified Framework ◽  
Robot Interaction ◽  
Tracking Tasks ◽  
Selection Of

SUMMARYIn this paper, we propose a novel unified framework for virtual guides. The human–robot interaction is based on a virtual robot, which is controlled by the admittance control. The unified framework combines virtual guides, control of the dynamic behavior, and path tracking. Different virtual guides and active constraints can be realized by using dead-zones in the position part of the admittance controller. The proposed algorithm can act in a changing task space and allows selection of the tasks-space and redundant degrees-of-freedom during the task execution. The admittance control algorithm can be implemented either on a velocity or on acceleration level. The proposed framework has been validated by an experiment on a KUKA LWR robot performing the Buzz-Wire task.

scholarly journals Adaptive Admittance Control for Human–Robot Interaction Using Model Reference Design and Adaptive Inverse Filtering

2017 ◽  
Vol 25 (1) ◽  
pp. 278-285 ◽  
Author(s):  
Isura Ranatunga ◽  
Frank L. Lewis ◽  
Dan O. Popa ◽  
Shaikh M. Tousif
Keyword(s):  
Human Robot Interaction ◽  
Inverse Filtering ◽  
Admittance Control ◽  
Robot Interaction ◽  
Model Reference ◽  
Reference Design
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