scholarly journals Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power-assist robotic system

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
S. M. Mizanoor Rahman ◽  
Ryojun Ikeura
Author(s):  
Binrui Wang ◽  
Jiqing Huang ◽  
Guoyang Shen ◽  
Dijian Chen

Purpose Active compliance control is the key technology for Tri-Co robots (coexisting–cooperative–cognitive robots) to interact with the environment and people. This study aims to make the robot arm shake hands compliantly with people; the paper proposed two closed-loop-compliant control schemes for the dynamic identification of cascade elbow joint. Design/methodology/approach The active compliance control strategy consists of inner and outer loops. The inner loop is the position control using sliding mode control with disturbance observer (SMCDO), in which a new saturation function is designed to replace the traditional signal function of sliding mode control (SMC) law so as to mitigate chatter. The outer loop is the admittance control to regulate the dynamic behaviours of the elbow joint, i.e. its impedance. The simulation is carried out to verify the performance of the proposed control scheme. Findings The results show that the chatter of traditional SMC can be effectively eliminated by using SMCDO with this saturation function. In addition, for the handshake task, the value of threshold force and elbow joint compliance is defined. Then, the threshold force tests, impact tests and elbow-joint compliance tests are carried out. The results show that, in the impedance model, the elbow joint compliance only depends on the stiffness parameters, not on the position control loop. Practical implications The effectiveness of the admittance control based on SMCDO can improve the adaptability of industrial manipulator in different working environments to some degree. Originality/value The admittance control with SMCDO completed trajectory tracking has higher accuracy than that based on SMC.


2018 ◽  
Vol 15 (4) ◽  
pp. 172988141877319 ◽  
Author(s):  
S M Mizanoor Rahman ◽  
Ryojun Ikeura

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.


2018 ◽  
Vol 15 (4) ◽  
pp. 172988141667813 ◽  
Author(s):  
S M Mizanoor Rahman ◽  
Ryojun Ikeura

Weight-perception-based fixed admittance control algorithm and variable admittance control algorithm are proposed for unimanual and bimanual lifting of objects with a power assist robotic system. To include weight perception in controls, the mass parameter for the inertial force is hypothesized as different from that for the gravitational force in the dynamics model for lifting objects with the system. For the bimanual lift, two alternative approaches of force sensor arrangements are considered: a common force sensor and two separate force sensors between object and human hands. Computational models for power assistance, excess in load forces, and manipulation efficiency and precision are derived. The fixed admittance control algorithm is evaluated in a 1-degree-of-freedom power assist robotic system. Results show that inclusion of weight perception in controls produce satisfactory performance in terms of power assistance, system kinematics and kinetics, human–robot interactions, and manipulation efficiency and precision. The fixed admittance control algorithm is then augmented to variable admittance control algorithm as a tool of active compliance to vary the admittance with inertia instead of with gravity. The evaluation shows further improvement in the performance for the variable admittance control algorithm. The evaluation also shows that bimanual lifts outperform unimanual lifts and bimanual lifts with separate force sensors outperform bimanual lifts with a common force sensor. Then, the results are proposed to develop power assist robotic systems for handling heavy objects in industries.


Skull Base ◽  
2007 ◽  
Vol 17 (S 1) ◽  
Author(s):  
Akio Morita ◽  
Ryo Mochizuki ◽  
Mamoru Mitsuishi ◽  
Shigeo Sora
Keyword(s):  

2019 ◽  
Vol 5 (6) ◽  
pp. 254-258
Author(s):  
A. Bystrova ◽  
◽  
N. Dembovskii ◽  
S. Sorokina ◽  
D. Dedyaev ◽  
...  
Keyword(s):  

2010 ◽  
Vol 14 (1) ◽  
pp. 6-13 ◽  
Author(s):  
František Trebuňa ◽  
Juraj Smrček ◽  
Zdenko Bobovský

Author(s):  
Wesley Oliveira ◽  
ALINE DA CONCEIÇÃO MATHEUS ◽  
Wilson Jose de Sa Marques ◽  
Luis Gonzaga Trabasso ◽  
Emilia Villani ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document