Pose Calibration Method of 6-D.O.F. Cable Driven Parallel Robot Using Hybrid Recurrent Neural Network

2018 ◽  
Author(s):  
Sung-Hyun Choi ◽  
Kyoung-Su Park ◽  
Jun-Mu Heo ◽  
Hue Ha
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Calibration Method ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Cable Model ◽  
Repetitive Motion

Cable driven parallel robots (CDPRs) have been widely used in precision Industries. However, an error occurs in repetitive motion of the CDPR because of the elongation and recovery of cable. Also, it is difficult to apply the numerical cable model because of its complexity. In this paper, we use the frequency based H-RNN method for accurate kinematics. H-RNN predicted more accurately compared with LSTM and RNN algorithms.

scholarly journals A Novel Calibration Algorithm for Cable-Driven Parallel Robots with Application to Rehabilitation

2019 ◽  
Vol 9 (11) ◽  
pp. 2182 ◽  
Author(s):  
Han Yuan ◽  
Xianghui You ◽  
Yongqing Zhang ◽  
Wenjing Zhang ◽  
Wenfu Xu
Keyword(s):  
Calibration Method ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Human Robot Interaction ◽  
Automatic Calibration ◽  
Unknown Parameters ◽  
Robot Interaction ◽  
Geometric Calibration ◽  
Rehabilitation Robots ◽  
Calibration Algorithm

Cable-driven parallel robots are suitable candidates for rehabilitation due to their intrinsic flexibility and adaptability, especially considering the safety of human–robot interaction. However, there are still some challenges to apply cable-driven parallel robots to rehabilitation, one of which is the geometric calibration. This paper proposes a new automatic calibration method that is applicable for cable-driven parallel rehabilitation robots. The key point of this method is to establish the mapping between the unknown parameters to be calibrated and the parameters that could be measured by the inner sensors and then use least squares algorithm to find the solutions. Specifically, the unknown parameters herein are the coordinates of the attachment points, and the measured parameters are the lengths of the redundant cables. Simulations are performed on a 3-DOF parallel robot driven by four cables for validation. Results show that the proposed calibration method could precisely find the real coordinate values of the attachment points, with errors less than 10 − 12 mm. Trajectory simulations also indicate that the positioning accuracy of the cable-driven parallel robot (CDPR) could be greatly improved after calibration using the proposed method.

Advanced Numerical Modeling of Nonlinear Elastic Cable With Permanent Stretch Using Cable Driven Parallel Robot

2017 ◽  
Author(s):  
Sung-Hyun Choi ◽  
Kyoung-Su Park
Keyword(s):  
Numerical Modeling ◽  
Steel Wire ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Cable Model ◽  
Lower Weight ◽  
Simulation Results ◽  
Nonlinear Elastic ◽  
Good Agreement ◽  
Elastic Characteristics

Since cable driven parallel robots (CDPRs) have many advantages, they have been used in many industrial fields. Fully constrained CDPRs mainly use Dyneema polyethylene because it has advantage of the lower weight than steel wire. However, the polyethylene cable has complex elastic characteristics (e.g. permanent stretch and hysteresis). In this paper, the advanced numerical modeling of nonlinear elastic cable with permanent stretch using cable driven parallel robot was derived and simulated for various cable condition. Based on the advanced numerical nonlinear cable model, the simulation was carried out under the various cable lengths and tensions. Compared to the experimental results, the simulation results are good agreement with the experimental data.

scholarly journals Forward Kinematics for Suspended Under-Actuated Cable-Driven Parallel Robots: A Neural Network Approach

2021 ◽  
Author(s):  
Utkarsh A. Mishra ◽  
Stéphane Caro
Keyword(s):  
Neural Network ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Least Square ◽  
Equilibrium Equations ◽  
Neural Network Approach ◽  
Comparison Results ◽  
Unsupervised Neural Network ◽  
Non Linear ◽  
Neural Network Algorithm

Abstract Kinematic analysis of under-constrained Cable-Driven Parallel Robots has been a topic of interest because of the inherent coupling between the loop-closure and static equilibrium equations. The paper proposes an unsupervised neural network algorithm to perform real-time forward geometrico-static analysis of such robots in a suspended configuration under the action of gravity. The formulation determines a non-linear function approximation to model the problem and proves to be efficient in solving for consecutive and close waypoints in a path. The methodology is applied on a six-degree-of-freedom (6-DOF) spatial under-constrained suspended cable-driven parallel robot. Specific comparison results to show the effectiveness of the proposed method in tracking a given path and degree of constraint satisfaction are presented against the results obtained from non-linear least-square optimization.

scholarly journals Parallel robot with fuzzy neural network sliding mode control

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880126 ◽  
Author(s):  
Jiangmin Xu ◽  
Qi Wang ◽  
Qing Lin
Keyword(s):  
Neural Network ◽  
Control Theory ◽  
Sliding Mode Control ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Adaptive Sliding Mode Control ◽  
Mode Control ◽  
Fuzzy Neural

With the advancement in research on parallel robots, control theory is increasingly applied in the field of robotics. Owing to its robustness, sliding mode variable structure control is extensively used in parallel robots. This article presents an adaptive sliding mode control method for nonlinear systems. A parallel robot control model with adaptive fuzzy sliding mode control was designed based on a fuzzy neural network control theory, and simulation results demonstrate its effectiveness of the method.

A learning-based control framework for cable-driven parallel robots with unknown Jacobians

2020 ◽  
Vol 234 (9) ◽  
pp. 1024-1036
Author(s):  
Hao Xiong ◽  
Lin Zhang ◽  
Xiumin Diao
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Parallel Robot ◽  
Joint Space ◽  
Parallel Robots ◽  
The Neural Network ◽  
Control Framework ◽  
In Series

Cable-driven parallel robots have been studied by many researchers in the past decades. The Jacobian of a cable-driven parallel robot may not be determined in some applications such as rehabilitation. In order to control the pose of a fully constrained cable-driven parallel robot with unknown Jacobian and driven by torque-controlled actuators, a learning-based control framework consisting of a robust controller and a neural network in series is proposed in this article. The neural network takes over the role of the Jacobian by mapping a wrench applied on the end-effector of the cable-driven parallel robot at a pose in the task space to a set of cable tensions in the joint space. In this way, the cable-driven parallel robot can be controlled by cable tensions derived from such a mapping, rather than solving the inverse dynamics problem based on the Jacobian. As an example, a control strategy is developed to demonstrate how the proposed control framework works. The control strategy includes a proportional–integral–derivative controller and a feedforward neural network. Simulation results show that the control strategy can successfully control a cable-driven parallel robot with four cables, three degrees of freedom, and unknown Jacobian.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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