Pigeon-Inspired UAV Swarm Control and Planning Within a Virtual Tube

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript is logical and innovative, the work is very meaningful, and it is recommended to accept it directly.

Author Response

Comments 1: The manuscript is logical and innovative, the work is very meaningful, and it is recommended to accept it directly.

Response 1: We sincerely appreciate the reviewer’s kind recognition of our work and their supportive recommendation for acceptance. It is very encouraging to hear that our manuscript’s innovation and significance were well received. We are grateful for the time and effort the reviewer dedicated to evaluating our paper, and we are delighted that it meets their approval. Thank you once again for your valuable time and positive feedback.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This well-organized paper presents the Pigeon-inspired UAV swarm control and planning within a virtual tube. This paper's advantages include the new insight of this specific use case which approaches the UAV planning and control with a pigeon-based behavior modelling.
Moreover, the paper presents details of holistic study of the behavior of UAV swarm motion. Method's disadvantages refers to missing parts of solid autonomous systems control background but only signal processing heuristic approaching.

Minor issues should be discussed; the authors might update them optimizing their work.

a) By deeper understanding and knowledge of swarm intelligence following citations, the authors might strengthen their work into missing related work without reference to extensive recent literature.
[1] Apostolidis, Georgios K., and Leontios J. Hadjileontiadis. "Swarm decomposition: A novel signal analysis using swarm intelligence." Signal Processing 132 (2017): 40-50.
[2] Kaltsa, Vagia, et al. "Swarm intelligence for detecting interesting events in crowded environments." IEEE transactions on image processing 24.7 (2015): 2153-2166.

b) The paper presentation might characterized by symbol conherence; the UAVs position/velocities and respective pigeon-based position/velocities might be declared with the common names of variables while presenting the sections 2,3.
c) As the interactive relation between the components (pigeons/drones)  was taken account in the presented modeling, the authors might add some citations in the related work within this matter.
d) No adequate performance evaluation for a journal paper:
i) Extented performance evaluation is missing because only figures are presented instead of tables with evaluation metrics; The authors should explain the illustrations and figures with numbers of evaluation metrics in respective Tables.
ii) The missing comparative performance evaluation between A*, RRT and PIO methods might be added with relevant metrics; In addition to use only minimum distance for evaluation, several evaluation metrics could be added (Fitness, Mean, Std. Dev., Position Diversity, Velocity Diversity, Cognitive Diversity etc) for the trajectories.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

1. Please further explain why the PIO algorithm is chosen for path planning problems among many swarm intelligence algorithms.
2. Since its introduction in 2014, PIO has seen many improved variants. Why does this paper choose to use the original PIO algorithm? Please provide a reasonable explanation or demonstrate through experiments that the performance of recently proposed improved versions of PIO is inferior to the original PIO in this problem.
3. The literature review is incomplete. Please add the latest research findings to improve the current state analysis. It is recommended to use a table format for clearer presentation.
4. There is no clear basis or experimental analysis for the use of algorithm and model hyperparameters (such as control parameters k1, k2, k3, etc.). Please provide additional parameter sensitivity experiments.
5. The experimental comparisons are insufficient. There are no comparisons with the latest swarm intelligence algorithms to demonstrate the superiority of the selected algorithm in this paper under the same experimental conditions.
6. Add tests such as p-values to validate the superiority of the algorithm from multiple perspectives.
7. In addition, there are many non-native English and colloquial expressions in this article, please polish them.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

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Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

The authors would do well to indicate the simulation software.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

I have no other questions.

Author Response

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Author Response File: Author Response.docx