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Article
Peer-Review Record

Genetic Fuzzy Methodology for Decentralized Cooperative UAVs to Transport a Shared Payload

by Anoop Sathyan *, Ou Ma and Kelly Cohen
Reviewer 1:
Reviewer 2:
Submission received: 25 December 2022 / Revised: 20 January 2023 / Accepted: 1 February 2023 / Published: 3 February 2023
(This article belongs to the Special Issue Multi-UAVs Control)

Round 1

Reviewer 1 Report

The article “Genetic Fuzzy Based Training of Decentralized Cooperative Agents for Transporting a Slung Payload” needs to do minor revision by doing the following task to publish your article in MDPI Drones.

1.      The title needs to be revised and changed according to the work done.

2.      The abstract need to revise and improve according to the work done in the manuscript. The abstract must be precise in terms of words.

3.      In the introduction, there is no motivation for the readers. Highlight motivation work done in your manuscript.

4.      The research objective is a core part of any research article. You need to add a research objective to this article.

5.      Authors are advised to use the last 3 years' references, especially from MDPI Drones.

The authors are advised to review this article thoroughly and perform the suggested tasks. This advice must be treated for the betterment of the article and authors.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper proposes a genetic fuzzy based cooperative control method of transporting a slung payload for multi-UAV systems. Simulations are conducted to verify the performance of the proposed method. Generalization ability of the method is also illustrated using scenarios different from the training scenario. However, many technical modules are missing in the paper. The authors should provide the details to illustrate the soundness of the method technically.

 

1. What are the details of the GFS model? How do the model parameters affect the output of the model?

 

2. Fuzzy inference logic tables are usually provided in fuzzy logic system design. What are the detailed membership functions in their design?

 

3. What is the detailed perturb method in mutation?

 

4. It seems that this method does not consider the take-off process for the UAV. What is the initial trust vector of each UAV of the beginning in training and test scenarios, respectively?

 

5. What are the subfigures in Figure 5 represent? The authors should provide more details to explain the figure.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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