An analytical and a Deep Learning model for solving the inverse kinematic problem of an industrial parallel robot

Toquica, Juan S.; Oliveira, Patrícia S.; Souza, Witenberg Santiago Rodrigues; Motta, José Maurício Santos Torres da; Borges, Díbio Leandro

doi:10.1016/j.cie.2020.106682

Cited by 33 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning (ML) has become a key element in current robotic fields for industrial, collaborative, mobile, and social applications [1][2][3][4]. Robots are adopted to provide repeatability, reliability [5] and to guarantee the time-variant movement's accuracy.…”

Section: Introductionmentioning

confidence: 99%

Inverse kinematic solver based on machine learning sequential procedure for robotic applications

Aggogeri

Pellegrini

Taesi

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper presents an inverse kinematic solver for a robotic arm based on an artificial neural network, ANN, architecture. The motion of the robotic arm is controlled by the kinematics of the ANN. The novelty of the proposed method is the validation using a proprietary robot of a novel procedure that applies three networks in a sequential mode to predict one joint value at a time. The inclusion of the genetic algorithm in the ANN definition and the adoption of sequential technique significantly reduced the manual settings and increased the obtained accuracy with respect to the traditional network deployment. The simulated outcomes proved the efficacy of the proposed approach in robotic motion control. The final architecture has three hidden layers: {40 (tansig), 35 (tansig), 30 (tansig)}. The resultant MSE in joint space is close to 3.235*10-4 rad2 and 0.1318mm2 in Cartesian space for the testing dataset. The maximum trajectory error for the validation curves, a planar circle and a spatial spring, is lower than 0.27mm for each axis.

show abstract

Section: Introductionmentioning

confidence: 99%

Inverse kinematic solver based on machine learning sequential procedure for robotic applications

Aggogeri

Pellegrini

Taesi

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…These techniques and methods solve issues such as maximum workspace and direct kinematics. A fast analytical method and a deep learning approach model were used in [25] to solve an industrial parallel robot's reverse problem. The analytical method was compared with three models of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Modeling in Two Configurations of a 5R 2-DoF Planar Parallel Mechanism and Solution to the Inverse Kinematic Modeling Using Artificial Neural Network

López

Mora-Pulido

et al. 2021

IEEE Access

View full text Add to dashboard Cite

This article introduces a new kinematic modeling method used to analyze coupled rigid multibody movements. The method was applied to the study of a 5R planar parallel mechanism's kinematics and consists of analyzing two fixed configurations of the mechanism to systematize the rotational relationships between the two structures. Mathematical models were developed using complex numbers. The inverse kinematic problem was modeled as a system of eight nonlinear equations and eight unknowns, which was solved with Newton-Raphson's method. Subsequently, with the inverse problem model, a numerical database related to the mechanism configurations, including singular positions, was generated to train a multilayer neural network. The Levenberg-Marquardt algorithm was used for network training. Finally, an interpolated linear path was used to understand the efficiency of the trained network.

show abstract

“…Recent studies on neural networks and deep learning have proven that robotic control can benefit from this method, and neural networks can replace the complicated mathematical modeling of some systems [2,[4][5][6]. Neural networks can learn from data.…”

Section: Introductionmentioning

confidence: 99%

Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

et al. 2021

View full text Add to dashboard Cite

This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.

show abstract

An analytical and a Deep Learning model for solving the inverse kinematic problem of an industrial parallel robot

Cited by 33 publications

References 19 publications

Inverse kinematic solver based on machine learning sequential procedure for robotic applications

Inverse kinematic solver based on machine learning sequential procedure for robotic applications

Modeling in Two Configurations of a 5R 2-DoF Planar Parallel Mechanism and Solution to the Inverse Kinematic Modeling Using Artificial Neural Network

Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

Contact Info

Product

Resources

About