Inverse kinematics identification of a spherical robot based on BP neural networks

Cai, Yao; Zhan, Qiang; Xi, Xi; Rahmani, Ahmed

doi:10.1109/iciea.2011.5975941

Cited by 8 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 5: Equations (9) and (12) are used to calculate the resultant force F d i ðtÞ for x i (t) in each dimension. Then use equation (14) to calculate the acceleration a d i ðtÞ.…”

Section: Gsade Algorithm Processmentioning

confidence: 99%

“…Moreover, Bingul et al 13 applied the BP neural network to solve the inverse kinematics problem of 6-DOF industrial robots. Cai et al 14 proposed a method for identifying the inverse kinematics of the spherical robot BHQ-1 based on the BP neural network. Ma et al 15 used the BP neural network to compensate the pose error of a 6-DOF parallel robot.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

Huang

Cen

Xie

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot’s manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.

show abstract

“…Step 5: Equations (9) and (12) are used to calculate the resultant force F d i ðtÞ for x i (t) in each dimension. Then use equation (14) to calculate the acceleration a d i ðtÞ.…”

Section: Gsade Algorithm Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

Huang

Cen

Xie

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Tu et al modeled the static friction in a robot joint by using a BP neural network to replace traditional methods [23]. Cai et al adopted BP neural networks to identify inverse kinematics of a spherical robot [24]. Yang et al used a neural network approximation technique to compensate the unknown dynamics of both the robot arms and the manipulated object [25].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Modeling and Controlling a Pneumatic Muscle Actuator‐Driven Setup Using Back Propagation Neural Networks

2018

View full text Add to dashboard Cite

Pneumatic muscle actuators (PMAs) own excellent compliance and a high power-to-weight ratio and have been widely used in bionic robots and rehabilitated robots. However, the high nonlinear characteristics of PMAs due to inherent construction and pneumatic driving principle bring great challenges in applications acquired accurately modeling and controlling. To tackle the tricky problem, a single PMA mass setup is constructed, and a back propagation neural network (BPNN) is employed to identify the dynamics of the setup. An offline model is built up using sampled data, and online modifications are performed to further improve the quality of the model. An adaptive controller based on BPNN is designed using gradient descent information of the built-up model. Experiments of identifying the PMA setup using BPNN and position tracking by adaptive BPNN controller are performed, and results demonstrate the good capacity in accurate controlling of the PMA setup.

show abstract

Random-Optimal Differential Evolution Neural Network Model for Inverse Calculation of Demolition Robot

Huang

Cen

Zong

et al. 2022

Springer Proceedings in Physics

View full text Add to dashboard Cite

Inverse kinematics identification of a spherical robot based on BP neural networks

Cited by 8 publications

References 11 publications

Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

A New Approach to Modeling and Controlling a Pneumatic Muscle Actuator‐Driven Setup Using Back Propagation Neural Networks

Random-Optimal Differential Evolution Neural Network Model for Inverse Calculation of Demolition Robot

Contact Info

Product

Resources

About