Eliminating the Inertial Forces Effects on the Measurement of Robot Interaction Force

2019 ◽  
pp. 67-76
Author(s):  
Piotr Gierlak ◽  
Andrzej Burghardt ◽  
Dariusz Szybicki ◽  
Krzysztof Kurc
Keyword(s):  
Interaction Force ◽  
Inertial Forces ◽  
Robot Interaction

scholarly journals Multi-connection load compensation and load information calculation for an upper-limb exoskeleton based on a six-axis force/torque sensor

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986318 ◽  
Author(s):  
Xin Wang ◽  
Qiuzhi Song ◽  
Shitong Zhou ◽  
Jing Tang ◽  
Kezhong Chen ◽  
...  
Keyword(s):  
Upper Limb ◽  
Interaction Force ◽  
Center Of Gravity ◽  
Human Robot Interaction ◽  
Whole Body ◽  
Torque Sensor ◽  
Robot Interaction ◽  
End Effector ◽  
Load Compensation ◽  
Upper Limb Exoskeleton

In this article, a method of multi-connection load compensation and load information calculation for an upper-limb exoskeleton is proposed based on a six-axis force/torque sensor installed between the exoskeleton and the end effector. The proposed load compensation method uses a mounted sensor to measure the force and torque between the exoskeleton and load of different connections and adds a compensator to the controller to compensate the component caused by the load in the human–robot interaction force, so that the human–robot interaction force is only used to operate the exoskeleton. Therefore, the operator can manipulate the exoskeleton with the same interaction force to lift loads of different weights with a passive or fixed connection, and the human–robot interaction force is minimized. Moreover, the proposed load information calculation method can calculate the weight of the load and the position of its center of gravity relative to the exoskeleton and end effector accurately, which is necessary for acquiring the upper-limb exoskeleton center of gravity and stability control of whole-body exoskeleton. In order to verify the effectiveness of the proposed method, we performed load handling and operational stability experiments. The experimental results showed that the proposed method realized the expected function.

Improving stability in physical human–robot interaction by estimating human hand stiffness and a vibration index

2019 ◽  
Vol 46 (4) ◽  
pp. 529-540 ◽  
Author(s):  
Feifei Bian ◽  
Danmei Ren ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Time Delay ◽  
Real Time ◽  
Time Domain ◽  
Design Methodology ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
Human Hand ◽  
Robot Interaction ◽  
Content Type

Purpose The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index. Design/methodology/approach Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction. Findings By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms. Originality/value The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

scholarly journals Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhenlei Chen ◽  
Qing Guo ◽  
Huiyu Xiong ◽  
Dan Jiang ◽  
Yao Yan
Keyword(s):  
Lower Limb ◽  
Degrees Of Freedom ◽  
Position Control ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Detection Accuracy ◽  
Robot Interaction ◽  
Experiment Platform ◽  
Lower Limb Exoskeleton

AbstractIn this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

scholarly journals Adaptive Position/Force Control of a Robotic Manipulator in Contact with a Flexible and Uncertain Environment

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
Piotr Gierlak
Keyword(s):  
Control Systems ◽  
Movement Control ◽  
Interaction Force ◽  
Resistance Force ◽  
Control Law ◽  
Robot Interaction ◽  
Control Laws ◽  
The Impact ◽  
Interaction Surface ◽  
Selection Of

The present paper concerns the synthesis of robot movement control systems in the cases of disturbances of natural position constraints, which are the result of surface susceptibility and inaccuracies in its description. The study contains the synthesis of control laws, in which the knowledge of parameters of the susceptible environment is not required, and which guarantee stability of the system in the case of an inaccurately described contact surface. The novelty of the presented solution is based on introducing an additional module to the control law in directions normal to the interaction surface, which allows for a fluent change of control strategy in the case of occurrence of distortions in the surface. An additional module in the control law is perceived as a virtual viscotic resistance force and resilient environment acting upon the robot. This interpretation facilitates intuitive selection of amplifications and allows for foreseeing the behavior of the system when disturbances occur. Introducing reactions of virtual constraints provides automatic adjustment of the robot interaction force with the susceptible environment, minimizing the impact of geometric inaccuracy of the environment.

scholarly journals Fiber Bragg Gratings in CYTOP Fibers Embedded in a 3D-Printed Flexible Support for Assessment of Human–Robot Interaction Forces

Materials ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2305 ◽  
Author(s):  
Arnaldo Leal-Junior ◽  
Antreas Theodosiou ◽  
Camilo Díaz ◽  
Carlos Marques ◽  
Maria Pontes ◽  
...  
Keyword(s):  
Thermoplastic Polyurethane ◽  
Fiber Bragg Gratings ◽  
Acrylonitrile Butadiene Styrene ◽  
Bragg Gratings ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Polymer Optical Fiber ◽  
Robot Interaction ◽  
Interaction Forces ◽  
Flexible Support

We developed a flexible support with embedded polymer optical fiber (POF) sensors for the assessment of human–robot interaction forces. The supports were fabricated with a three-dimensional (3D) printer, where an acrylonitrile butadiene styrene (ABS) rigid structure was used in the region of the support in which the exoskeleton was attached, whereas a thermoplastic polyurethane (TPU) flexible structure was printed in the region where the users placed their legs. In addition, fiber Bragg gratings (FBGs), inscribed in low-loss, cyclic, transparent, optical polymer (CYTOP) using the direct-write, plane-by-plane femtosecond laser inscription method, were embedded in the TPU structure. In this case, a 2-FBG array was embedded in two supports for human–robot interaction force assessment at two points on the users’ legs. Both FBG sensors were characterized with respect to temperature and force; additionally, the creep response of the polymer, where temperature influences the force sensitivity, was analyzed. Following the characterization, a compensation method for the creep and temperature influence was derived, showing relative errors below 4.5%. Such errors were lower than the ones obtained with similar sensors in previously published works. The instrumented support was attached to an exoskeleton for knee rehabilitation exercises, where the human–robot interaction forces were measured in flexion and extension cycles.

scholarly journals An event-controlled online trajectory generator based on the human-robot interaction force processing

2014 ◽  
Vol 41 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Sarra Jlassi ◽  
Sami Tliba ◽  
Yacine Chitour
Keyword(s):  
Impedance Control ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Interaction Point ◽  
Robot Interaction ◽  
Heavy Load ◽  
Content Type ◽  
Constrained Optimal Control ◽  
Trajectory Generator

Purpose – The problem of robotic co-manipulation is often addressed using impedance control based methods where the authors seek to establish a mathematical relation between the velocity of the human-robot interaction point and the force applied by the human operator (HO) at this point. This paper aims to address the problem of co-manipulation for handling tasks seen as a constrained optimal control problem. Design/methodology/approach – The proposed point of view relies on the implementation of a specific online trajectory generator (OTG) associated with a kinematic feedback loop. This OTG is designed so as to translate the HO intentions to ideal trajectories that the robot must follow. It works as an automaton with two states of motion whose transitions are controlled by comparing the magnitude of the force to an adjustable threshold, in order to enable the operator to keep authority over the robot's states of motion. Findings – To ensure the smoothness of the interaction, the authors propose to generate a velocity profile collinear to the force applied at the interaction point. The feedback control loop is then used to satisfy the requirements of stability and of trajectory tracking to guarantee assistance and operator security. The overall strategy is applied to the penducobot problem. Originality/value – The approach stands out for the nature of the problem to be tackled (heavy load handling tasks) and for its vision on the co-manipulation. It is based on the implementation of two main ingredients. The first one lies in the online generation of an appropriate trajectory of the interaction point located at the end-effector and describing the HO intention. The other consists in the design of a control structure allowing a good tracking of the generated trajectory.

scholarly journals Sensing small interaction forces through proprioception

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fazlur Rashid ◽  
Devin Burns ◽  
Yun Seong Song
Keyword(s):  
Muscle Contraction ◽  
Interaction Force ◽  
Initial Position ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Interaction Forces ◽  
Force Direction ◽  
Human Motor

AbstractUnderstanding the human motor control strategy during physical interaction tasks is crucial for developing future robots for physical human–robot interaction (pHRI). In physical human–human interaction (pHHI), small interaction forces are known to convey their intent between the partners for effective motor communication. The aim of this work is to investigate what affects the human’s sensitivity to the externally applied interaction forces. The hypothesis is that one way the small interaction forces are sensed is through the movement of the arm and the resulting proprioceptive signals. A pHRI setup was used to provide small interaction forces to the hand of seated participants in one of four directions, while the participants were asked to identify the direction of the push while blindfolded. The result shows that participants’ ability to correctly report the direction of the interaction force was lower with low interaction force as well as with high muscle contraction. The sensitivity to the interaction force direction increased with the radial displacement of the participant’s hand from the initial position: the further they moved the more correct their responses were. It was also observed that the estimated stiffness of the arm varies with the level of muscle contraction and robot interaction force.

scholarly journals Variable Admittance Control Based on Trajectory Prediction of Human Hand Motion for Physical Human-Robot Interaction

2021 ◽  
Vol 11 (12) ◽  
pp. 5651
Author(s):  
Yu Wang ◽  
Yuanyuan Yang ◽  
Baoliang Zhao ◽  
Xiaozhi Qi ◽  
Ying Hu ◽  
...  
Keyword(s):  
Control Method ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Movement Direction ◽  
Human Hand ◽  
Admittance Control ◽  
Trajectory Prediction ◽  
Robot Interaction ◽  
Hand Motion ◽  
Moving Direction

In order to achieve effective physical human–robot interaction, human dynamic characteristics needs to be considered in admittance control. This paper proposes a variable admittance control method for physical human–robot interaction based on trajectory prediction of human hand motion. By predicting the moving direction of the robot end tool under human guidance, the admittance control parameters are adjusted to reduce the interaction force. The end tool trajectory of the robot under human guidance is used for offline training of long and short-term memory neural network to generate trajectory predictors. Then the trajectory predictors are used in variable admittance control to predict the trajectory and movement direction of the robot end tool in real time. The variable admittance controller adjusts the damping matrix to reduce the damping value in the moving direction. Experiment results show that, using the constant admittance method as a benchmark, the interaction force of the proposed method is reduced by 23%, the trajectory error is reduced by 51%, and the operating jerk is reduced by at least 21%, which proves that the proposed method improves the accuracy and compliance of the operation.

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