Adaptive Human–Robot Interaction Control for Robots Driven by Series Elastic Actuators

2017 ◽  
Vol 33 (1) ◽  
pp. 169-182 ◽  
Author(s):  
Xiang Li ◽  
Yongping Pan ◽  
Gong Chen ◽  
Haoyong Yu
2015 ◽  
Vol 31 (5) ◽  
pp. 1089-1100 ◽  
Author(s):  
Haoyong Yu ◽  
Sunan Huang ◽  
Gong Chen ◽  
Yongping Pan ◽  
Zhao Guo

Robotica ◽  
2014 ◽  
Vol 32 (8) ◽  
pp. 1301-1316 ◽  
Author(s):  
Andrea Calanca ◽  
Paolo Fiorini

SUMMARYForce-controlled series elastic actuators (SEAs) are the widely used components of novel physical human–robot interaction applications such as assistive and rehabilitation robotics. These systems are characterized by the presence of the “human in the loop” so that control response and stability depend on uncertain human dynamics. A common approach to guarantee stability is to use a passivity-based controller. Unfortunately, existing passivity-based controllers for SEAs do not define the performance of the force/torque loop. We propose a method to obtain predictable force/torque dynamics based on adaptive control and oversimplified human models. We propose a class of stable human-adaptive algorithms and experimentally show advantages of the proposed approach.


2021 ◽  
Vol 54 (4) ◽  
pp. 112-117
Author(s):  
Andres L. Jutinico ◽  
Oscar Flórez-cediel ◽  
Adriano A.G. Siqueira

2019 ◽  
Vol 38 (6) ◽  
pp. 747-765 ◽  
Author(s):  
Federica Ferraguti ◽  
Chiara Talignani Landi ◽  
Lorenzo Sabattini ◽  
Marcello Bonfè ◽  
Cesare Fantuzzi ◽  
...  

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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