Design of a planar parallel robot to investigate human arm point to point reaching movement

2015 ◽  
Author(s):  
M. Momtahan ◽  
Hr. Farhang ◽  
S. Dehghani ◽  
F. Bahrami ◽  
H. Moradi ◽  
...  
Keyword(s):  
Parallel Robot ◽  
Reaching Movement ◽  
Human Arm ◽  
Point To Point

scholarly journals Motor learning in reaching tasks leads to homogenization of task space error distribution

2021 ◽  
Author(s):  
Puneet Singh ◽  
Oishee Ghosal ◽  
Aditya Murthy ◽  
Ashitava Ghodal
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Error Distribution ◽  
Task Space ◽  
Field Perturbation ◽  
Directional Dependence ◽  
Reaching Tasks ◽  
Space Error ◽  
Human Arm ◽  
Point To Point

A human arm, up to the wrist, is often modelled as a redundant 7 degree-of-freedom serial robot. Despite its inherent nonlinearity, we can perform point-to-point reaching tasks reasonably fast and with reasonable accuracy in the presence of external disturbances and noise. In this work, we take a closer look at the task space error during point-to-point reaching tasks and learning during an external force-field perturbation. From experiments and quantitative data, we confirm a directional dependence of the peak task space error with certain directions showing larger errors than others at the start of a force-field perturbation, and the larger errors are reduced with repeated trials implying learning. The analysis of the experimental data further shows that a) the distribution of the peak error is made more uniform across directions with trials and the error magnitude and distribution approaches the value when no perturbation is applied, b) the redundancy present in the human arm is used more in the direction of the larger error, and c) homogenization of the error distribution is not seen when the reaching task is performed with the non-dominant hand. The results support the hypothesis that not only magnitude of task space error, but the directional dependence is reduced during motor learning and the workspace is homogenized possibly to increase the control efficiency and accuracy in point-to-point reaching tasks. The results also imply that redundancy in the arm is used to homogenize the workspace, and additionally since the bio-mechanically similar dominant and non-dominant arms show different behaviours, the homogenizing is actively done in the central nervous system.

3D human arm reaching movement planning with principal patterns in successive phases

2020 ◽  
Vol 48 (3) ◽  
pp. 265-280
Author(s):  
Sedigheh Dehghani ◽  
Fariba Bahrami
Keyword(s):  
Movement Planning ◽  
Reaching Movement ◽  
Arm Reaching ◽  
Human Arm

Human-Like Posture Correction for Seven-Degree-of-Freedom Robotic Arm

2021 ◽  
pp. 1-16
Author(s):  
Yu-Heng Deng ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Support Vector Machine Model ◽  
Humanoid Robots ◽  
Degree Of Freedom ◽  
Robotic Arm ◽  
Support Vector ◽  
Machine Model ◽  
Human Arm ◽  
Inverse Kinematics Problem ◽  
Point Trajectories ◽  
Point To Point

Abstract Owing to advancements in robotics, researchers have been focusing on integrating humanoid robots into actual environments. Most humanoid robots are equipped with seven-degree-of-freedom (DoF) arms that allow them to be flexible in different scenarios. The controller of a 7-DoF robotic arm must select one solution among the infinite sets of solutions for a given inverse kinematics problem. To date, no suitable approach has been developed for identifying appropriate human-like postures for a robotic arm with an offset wrist configuration. In this paper, we propose a novel algorithm that considers the movement of the human arm to consistently find a suitable human-like posture. First, a one-class support vector machine model is employed to classify human-like postures. Then, the algorithm uses the redundancy characteristic of a 7-DoF robotic arm with a linear regression model to enhance the search of human-like postures. Finally, the proposed algorithm is demonstrated in simulation, where it successfully optimized point-to-point trajectories by modifying only the endpoint posture.

Dynamic Point-to-Point Trajectory Planning of a Two-DOF Cable-Suspended Parallel Robot

2014 ◽  
Vol 30 (3) ◽  
pp. 728-736 ◽  
Author(s):  
Clement Gosselin ◽  
Simon Foucault
Keyword(s):  
Trajectory Planning ◽  
Parallel Robot ◽  
Point To Point

Dynamic Point-to-Point Trajectory Planning of a Three-DOF Cable-Suspended Parallel Robot

2016 ◽  
Vol 32 (6) ◽  
pp. 1550-1557 ◽  
Author(s):  
Xiaoling Jiang ◽  
Clement Gosselin
Keyword(s):  
Trajectory Planning ◽  
Parallel Robot ◽  
Point To Point

scholarly journals Explanation of Fitts’ law in Reaching Movement based on Human Arm Dynamics

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Misaki Takeda ◽  
Takanori Sato ◽  
Hisashi Saito ◽  
Hiroshi Iwasaki ◽  
Isao Nambu ◽  
...  
Keyword(s):  
Inverse Dynamics ◽  
Reaching Movements ◽  
Temporal Constraints ◽  
Reaching Movement ◽  
Fitts Law ◽  
Human Arm ◽  
Behavioural Experiment ◽  
Error Distributions ◽  
Dynamics Models ◽  
The Relationship

AbstractWhy does Fitts’ law fit various human behavioural data well even though it is not a model based on human physical dynamics? To clarify this, we derived the relationships among the factors applied in Fitts’ law—movement duration and spatial endpoint error—based on a multi-joint forward- and inverse-dynamics models in the presence of signal-dependent noise. As a result, the relationship between them was modelled as an inverse proportion. To validate whether the endpoint error calculated by the model can represent the endpoint error of actual movements, we conducted a behavioural experiment in which centre-out reaching movements were performed under temporal constraints in four directions using the shoulder and elbow joints. The result showed that the distributions of model endpoint error closely expressed the observed endpoint error distributions. Furthermore, the model was found to be nearly consistent with Fitts’ law. Further analysis revealed that the coefficients of Fitts’ law could be expressed by arm dynamics and signal-dependent noise parameters. Consequently, our answer to the question above is: Fitts’ law for reaching movements can be expressed based on human arm dynamics; thus, Fitts’ law closely fits human’s behavioural data under various conditions.

scholarly journals Adaptive sliding mode control of a novel cable driven robot model

2019 ◽  
Vol 13 (2) ◽  
pp. 5150-5162
Author(s):  
F. Inel ◽  
S. Babesse
Keyword(s):  
Sliding Mode Control ◽  
Control Strategy ◽  
Sliding Mode ◽  
Parallel Robot ◽  
Control Technique ◽  
Adaptive Sliding Mode Control ◽  
Adaptive Sliding Mode ◽  
Mode Control ◽  
Robot Performance ◽  
Point To Point

In this paper, we propose an adaptive sliding mode control strategy for a 3D cable-driven parallel robot. The proposed control technique is widely used for dealing with nonlinear systems uncertainties and for improving the robot performance in terms of tracking a desired path. The main contribution of this work is firstly: the graphical user interface (GUI) witch presents a point-to-point command, thus by the visualization of the end-effector position. Secondly, the sliding mode control is modeling for applied to the dynamic model for different trajectories in order to test the accurate tracking of the robot to a desired path. The effectiveness of the proposed control strategy is demonstrated through different simulation results.

Optimal Model for Selecting Human Arm Posture During Reaching Movement

2010 ◽  
pp. 453-458
Author(s):  
Jiaole Wang ◽  
Masazumi Katayama
Keyword(s):  
Optimal Model ◽  
Reaching Movement ◽  
Human Arm

DESIGN OF A THRESHOLD FES CONTROL SYSTEM FOR ARM MOVEMENT

2009 ◽  
Vol 09 (04) ◽  
pp. 449-479 ◽  
Author(s):  
L. LAN ◽  
K. Y. ZHU ◽  
C. Y. WEN
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Model Simulation ◽  
Arm Movement ◽  
External Perturbation ◽  
Inverse Dynamic ◽  
Threshold Control ◽  
Human Arm ◽  
Point To Point ◽  
Muscle Torques

The existing functional electrical stimulation (FES) techniques often required to solve the complex "inverse dynamic problem" to calculate the muscle torques for moving along a desired trajectory. According to the threshold control theory of voluntary motor control, a bio-mimetic threshold control strategy for the FES controller is designed and tested in the human arm movement. The arm is modeled as three segments connected by two hinges joints. The movement is driven by seven muscles and limited in the horizontal plane. All muscles are described by a modified Hill-type muscle model. Simulation results suggest that the threshold FES control system can realize point to point movement and can approximately follow the desired traces in presence of feedback delays up to 20 ms. The movement can also maintain stability under external perturbation or external load. The control system can be employed in clinical application because of the following advantages: (1) The control strategy includes some mature control techniques which had been realized in hardware. (2) Only sophisticated sensors of goniometer and the surface electrodes are needed to provide feedbacks and muscle stimulation. (3) The performance of the control system will not be critically influenced by the slight change of musculo-tendon parameters and feedback delays, and even the parameters of controller are fixed.

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