AI-Based Posture Control Algorithm for a 7-DOF Robot Manipulator

Round 1

Reviewer 1 Report

- Sufficient literature has been cited. However, it is not appropriate to refer to the references (6-14) with the same sentence, it should be corrected.

- The algorithm used is adequately explained.

- As an experimental study, only MATLAB simulation results are given. The proposed algorithm should be applied on the real robot and its results should be given.

Author Response

First, the authors greatly appreciate the reviewer’s valuable comments as the manuscript is improved by them. Please kindly check the attachment for the authors' responses. In the attachment, the reviewer’s comments are colored as “blue,” whereas the responses by the authors are given as “green.” In the revised manuscript, the corresponding parts that are revised from the original version are marked as “red” color. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Researchers

Thanks for the valuable manuscript. There are some points needed to be addressed to improve the quality. Please kindly find my comments as follows:  

 

        The theoretical contributions should be stressed in detail in Introduction.

·         Please check carefully all notations and equations. Moreover, the usage of English should be improved.

·         Advantages of the proposed algorithm upon the well-known algorithms should be stressed.  In introduction, it is not enough to state the current work. It should be expended and reconstructed. Including the motivation, the main difficulties, the main work and the improvements compared with previous related works should be emphasized in this section.

·         The state of the art is not complete and I suggest to consider review the following works as the potential resources to be reviewed in the introduction section for the AI based algorithms such as GA, PSO, ANN and RPLNN.

Urrea, C., & Jara, D. (2021). Design, analysis, and comparison of control strategies for an industrial robotic arm driven by a multi-level inverter. Symmetry, 13(1), 86.

 Chen, L., Sun, H., Zhao, W., & Yu, T. (2021). Robotic arm control system based on AI wearable acceleration sensor. Mathematical Problems in Engineering, 2021.

 

 Azizi, A. (2020). Applications of artificial intelligence techniques to enhance sustainability of industry 4.0: design of an artificial neural network model as dynamic behavior optimizer of robotic arms. Complexity, 2020.

 

 de Giorgio, A., & Wang, L. (2020). Artificial intelligence control in 4D cylindrical space for industrial robotic applications. IEEE Access, 8, 174833-174844.

Lim, Z. Y., & Quan, N. Y. (2021, June). Convolutional Neural Network Based Electroencephalogram Controlled Robotic Arm. In 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) (pp. 26-31). IEEE.

·         The importance of the problem considered in this paper should be further addressed.

·         The types of software employed for solving the problem and also simulation experiments should be stated clearly.

Author Response

First, the authors greatly appreciate the reviewer’s valuable comments as the manuscript is improved by them. Please kindly check the attachment for the authors' responses. In the attachment, the reviewer’s comments are colored as “blue,” whereas the responses by the authors are given as “green.” In the revised manuscript, the corresponding parts that are revised from the original version are marked as “red” color. 

Author Response File: Author Response.pdf

Reviewer 3 Report

This paper is based on reinforcement learning (RL) and artificial neural networks (ANN) for supervised learning (SL).  The results show that the proposed method is sufficient to control the robot manipulator as efficiently as the IK equation.  The topic of this work is very interesting. This paper is well-organized and easy to understand. I have the following comments for the improvement of the paper.

 

1. Some references  such as [1-5] are suggested to be discussed in details.

2.  The simulation conditions are suggested to be presented.

3. The authors are suggested to present more future works in the conclusion part.

4. The references part is needed to be updated. 

In summary, I recommend the acceptance of this paper for publication after the above comments are well addressed.

Author Response

First, the authors greatly appreciate the reviewer’s valuable comments as the manuscript is improved by them. Please kindly check the attachment for the authors' responses. In the attachment, the reviewer’s comments are colored as “blue,” whereas the responses by the authors are given as “green.” In the revised manuscript, the corresponding parts that are revised from the original version are marked as “red” color. 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Thanks for the corrections and explanations.