Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper presents an improved path planning algorithm for UAVs. Particularly it takes as a base the Potential-Rapidly exploring Random Tree Star (P-RRT*) and improves the convergence and the sampling. The improved algorithm uses the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Subsequently, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic and current path costs to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. In my view, the paper is good, but it needs improvements. 

* In the UAV applications, reference [4] is only about logistics. I suggest including a reference on UAV transportation like Energy-based control and LMI-based control for a quadrotor transporting a payload, Mathematics; A predictive control strategy for aerial payload transportation with an unmanned aerial vehicle, mathematics. 

* Please verify all the acronyms and variables are defined in the first use. 

* Can the authors calculate the computational cost of their algorithm? Can this method be coded in typical embedded UAV hardware?

* Can the proposed algorithm be proven to have a feasible solution in a finite time?

Comments on the Quality of English Language

The paper can benefit from minor language edits. 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Summary/Contribution: The main contribution of this work is the development of an improved UAV path planning algorithm, building upon the Potential-Rapidly Exploring Random Tree Star (P-RRT*) approach. This algorithm enhances search efficiency through artificial potential field (APF) direction-guided expansion, accelerates convergence by rejecting high-cost nodes based on heuristic and path cost considerations, and optimizes paths by pruning redundant nodes. These advancements collectively result in reduced path costs, fewer inflection points, faster convergence, and suitability for UAV path planning applications.

Comments/Suggestions:

1. Consider rephrasing the title for clarity to make it more concise while still capturing the essence of the paper.
2. In the abstract, explicitly state the main contributions and findings at the beginning.
3. Introduce a table or concise listing of the mentioned algorithms and their main features for easier reference.
4. Clarify the meaning of "wide range of random sampling".
5. In the introduction, begin with the broader significance of UAVs and then narrow down to the specific problem of path planning.
6. Summarize the historical context of different algorithms more concisely in the introduction.
7. Maintain consistency in terminologies. For instance, use the abbreviation UAVs consistently after its initial mention.
8. When discussing the limitations of P-RRT*, preemptively address how the new algorithm overcomes these limitations.
9. Provide pseudo-code for the proposed algorithm to help readers understand or implement the procedure.
10. Emphasize the advantages of the proposed algorithm by comparing it against existing methods in various scenarios.
11. In the conclusion, reiterate main contributions, potential future applications, highlight any limitations, and suggest areas for future research.
12. The authors need to add a short paragraph about the security aspects of drone communications.  For this purpose, they may include the following references (and others) in their study:  a. https://ieeexplore.ieee.org/document/9842403    b. https://www.mdpi.com/1424-8220/21/6/2057

Comments on the Quality of English Language

1. Break down long-winded sentences into shorter, more digestible ones
2. Ensure the entire document is free from grammatical and syntactical errors

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have presented an interesting enhancement to the existing P-RRT*. The results show that the proposed method is efficient compared to the existing one. I have a few minor comments as follows.

1. The authors should show how many nodes have increased compared to the existing one.

2. There should be an overall total execution time (from initial to final over all iterations) comparison among these methods.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I am happy with the revised manuscript.

Comments on the Quality of English Language

Minor edits are needed

Author Response

Thank you very much for taking the time to review this manuscript. 

Reviewer 2 Report

Comments and Suggestions for Authors

The authors considered my comments and suggestions.

Comments on the Quality of English Language

Can be improved.

Author Response

Thank you very much for taking the time to review this manuscript.