scholarly journals Cable Tension Analysis Oriented the Enhanced Stiffness of a 3-DOF Joint Module of a Modular Cable-Driven Human-Like Robotic Arm

2020 ◽  
Vol 10 (24) ◽  
pp. 8871
Author(s):  
Kaisheng Yang ◽  
Guilin Yang ◽  
Chi Zhang ◽  
Chinyin Chen ◽  
Tianjiang Zheng ◽  
...  
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Robot Interaction ◽  
Cable Tension ◽  
Tension Distribution ◽  
Stiffness Model ◽  
Redundantly Actuated ◽  
Objective Function Values

Inspired by the structure of human arms, a modular cable-driven human-like robotic arm (CHRA) is developed for safe human–robot interaction. Due to the unilateral driving properties of the cables, the CHRA is redundantly actuated and its stiffness can be adjusted by regulating the cable tensions. Since the trajectory of the 3-DOF joint module (3DJM) of the CHRA is a curve on Lie group SO(3), an enhanced stiffness model of the 3DJM is established by the covariant derivative of the load to the displacement on SO(3). In this paper, we focus on analyzing the how cable tension distribution problem oriented the enhanced stiffness of the 3DJM of the CHRA for stiffness adjustment. Due to the complexity of the enhanced stiffness model, it is difficult to solve the cable tensions from the desired stiffness analytically. The problem of stiffness-oriented cable tension distribution (SCTD) is formulated as a nonlinear optimization model. The optimization model is simplified using the symmetry of the enhanced stiffness model, the rank of the Jacobian matrix and the equilibrium equation of the 3DJM. Since the objective function is too complicated to compute the gradient, a method based on the genetic algorithm is proposed for solving this optimization problem, which only utilizes the objective function values. A comprehensive simulation is carried out to validate the effectiveness of the proposed method.

Determination of Variable Stiffness of a Human Elbow for Human-Robot Interaction

2014 ◽  
Author(s):  
Michael Boyarsky ◽  
Megan Heenan ◽  
Scott Beardsley ◽  
Philip Voglewede
Keyword(s):  
Experimental Data ◽  
Disturbance Rejection ◽  
Variable Stiffness ◽  
Important Variable ◽  
Human Motion ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Constant Stiffness ◽  
Stiffness Model ◽  
Motor Model

This paper aims to emulate human motion with a robot for the purpose of improving human-robot interaction (HRI). In order to engineer a robot that demonstrates functionally similar motion to humans, aspects of human motion such as variable stiffness must be captured. This paper successfully determined the variable stiffness humans use in the context of a 1 DOF disturbance rejection task by optimizing a time-varying stiffness parameter to experimental data in the context of a neuro-motor Simulink model. The significant improved agreement between the model and the experimental data in the disturbance rejection task after the addition of variable stiffness demonstrates how important variable stiffness is to creating a model of human motion. To enable a robot to emulate this motion, a predictive stiffness model was developed that attempts to reproduce the stiffness that a human would use in a given situation. The predictive stiffness model successfully decreases the error between the neuro-motor model and the experimental data when compared to the neuro-motor model with a constant stiffness value.

Gestures Identification for Myoelectric Based Control of the Robotic Arm

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Adhau P ◽  
◽  
Kadwane S. G ◽  
Shital Telrandhe ◽  
Rajguru V. S ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Human Hand ◽  
Robot Interaction ◽  
The Neural Network

Human robot interaction have been ever the topic of research to research scholars owing to its importance to help humanity. Robust human interacting robot where commands from Electromyogram (EMG) signals is recently being investigated. This article involves study of motions a system that allows signals recorded directly from a human body and thereafter can be used for control of a small robotic arm. The various gestures are recognized by placing the electrodes or sensors on the human hand. These gestures are then identified by using neural network. The neural network will thus train the signals. The offline control of the arm is done by controlling the motors of the robotic arm.

Design and Modeling of a Continuously Tunable Stiffness Arm for Safe Physical Human–Robot Interaction

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yu She ◽  
Hai-Jun Su ◽  
Deshan Meng ◽  
Cheng Lai
Keyword(s):  
Human Robot Interaction ◽  
Robotic Arm ◽  
Injury Criteria ◽  
Alternative Approach ◽  
Stiffness Model ◽  
Rigid Body Model ◽  
Physical Prototype ◽  
Head Injury Criteria ◽  
Impact Injury ◽  
Tunable Stiffness

Abstract To reduce injury in physical human–robot interactions (pHRIs), a common practice is to introduce compliance to joints or arm of a robotic manipulator. In this paper, we present a robotic arm made of parallel guided beams whose stiffness can be continuously tuned by morphing the shape of the cross section through two four-bar linkages actuated by servo motors. An analytical lateral stiffness model is derived based on the pseudo-rigid-body model and validated by experiments. A physical prototype of a three-armed manipulator is built. Extensive stiffness and impact tests are conducted, and the results show that the stiffness of the robotic arm can be changed up to 3.6 times at a morphing angle of 37 deg. At an impact velocity of 2.2 m/s, the peak acceleration has a decrease of 19.4% and a 28.57% reduction of head injury criteria (HIC) when the arm is tuned from the high stiffness mode to the low stiffness mode. These preliminary results demonstrate the feasibility to reduce impact injury by introducing compliance into the robotic link and that the compliant link solution could be an alternative approach for addressing safety concerns of physical human–robot interactions.

scholarly journals Study on Stiffness-Oriented Cable Tension Distribution for a Symmetrical Cable-Driven Mechanism

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1158 ◽  
Author(s):  
Yang ◽  
Yang ◽  
Chen ◽  
Wang ◽  
Zhang ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Variable Stiffness ◽  
Complex Method ◽  
Redundant Actuation ◽  
Spherical Joint ◽  
Cable Tension ◽  
Tension Distribution ◽  
Computation Efficiency ◽  
Stiffness Model ◽  
Position Regulation

In this paper, we focus on the issues pertaining to stiffness-oriented cable tension distributionfor a symmetrical 6-cable-driven spherical joint module (6-CSJM), which can be employed to constructmodular cable-driven manipulators. Due to the redundant actuation of the 6-CSJM, three cables areemployed for position regulation by adjusting the cable lengths, and the remaining three cables areutilized for stiffness regulation by adjusting the cable tensions, i.e., the position and stiffness can beregulated simultaneously. To increase the range of stiffness regulation, a variable stiffness device(VSD) is designed, which is serially connected to the driving cable. Since the stiffness model of the6-CSJM with VSDs is very complicated, it is difficult to directly solve the cable tensions from thedesired stiffness. The stiffness-oriented cable tension distribution issue is formulated as a nonlinearconstrained optimization problem, and the Complex method is employed to obtain optimal tensiondistributions. Furthermore, to significantly improve the computation efficiency, a decision variableelimination technique is proposed to deal with the equality constraints, which reduces decision variablesfrom 6 to 3. A comprehensive simulation study is conducted to verify the effectiveness of the proposedmethod, showing that the 6-CSJM can accurately achieve the desired stiffness through cable tensionoptimization.

scholarly journals Bilateral Personalized Dialogue Generation with Contrastive Learning

2022 ◽  
Author(s):  
Bin Li ◽  
Hanjun Deng
Keyword(s):  
Mutual Information ◽  
Objective Function ◽  
User Experience ◽  
State Of The Art ◽  
Human Robot Interaction ◽  
Learning Objective ◽  
Fusion Method ◽  
Conditional Mutual Information ◽  
Robot Interaction ◽  
Proper Response

Abstract Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this field mainly focus on generating responses consistent with the robot’s pre-assigned persona, while ignoring the user’s persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a Bilateral Personalized Dialogue Generation (BPDG) method for dyadic conversation, which integrates user and robot personas into dialogue generation via designing a dynamic persona-aware fusion method. To bridge the gap between the learning objective function and evaluation metrics, the Conditional Mutual Information Maximum (CMIM) criterion is adopted with contrastive learning to select the proper response from the generated candidates. Moreover, a bilateral persona accuracy metric is designed to measure the degree of bilateral personalization. Experimental results demonstrate that, compared with several state-of-the-art methods, the final results of the proposed method are more personalized and consistent with bilateral personas in terms of both automatic and manual evaluations.

A Continuously Tunable Stiffness Arm With Cable-Driven Mechanisms for Safe Physical Human-Robot Interaction

2020 ◽  
Author(s):  
Yu She ◽  
Zhaoyuan Gu ◽  
Siyang Song ◽  
Hai-Jun Su ◽  
Junmin Wang
Keyword(s):  
Variable Stiffness ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Robot Arm ◽  
Robot Interaction ◽  
Safety Issues ◽  
Compliant Joints ◽  
Mechanical Compliance ◽  
3D Printed ◽  
Tunable Stiffness

Abstract In this paper, we present a continuously tunable stiffness arm for safe physical human-robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human-robot interaction via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/actuators, variable stiffness links for safe physical human-robot interactions are much less studied. This paper details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26% given a morphing angle of 90°. The study demonstrates that the compliant link design could be a promising approach to address safety concerns for safe physical human-robot interactions.

Sign in / Sign up

Export Citation Format

Share Document