Multi-Node Path Planning of Electric Tractor Based on Improved Whale Optimization Algorithm and Ant Colony Algorithm

Round 1

Reviewer 1 Report

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Comments for author File: Comments.pdf

Author Response

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Reviewer 2 Report

This paper presents a novel method with an attractive application, which probably can be published and gain future research attention. Therefore, I recommend the authors some advice to improve the quality of this paper, which is as follows.

After reading the paper and the abstract can be improved to deliver the main idea of the research for those unfamiliar with this domain easily. Also, the details of the conducted contributions, experiments, comparisons, and obtained results should be added into the abstract section properly.

The introduction section can be presented in another way. The authors can give the introduction section in some terms like the general idea that already demonstrates an interest these days, the particular domain that uses the current application, the main problem in this research, and the central gap founded by the authors in this paper. Some related works support the main claim in this paper and support this work by focusing on some issues. At the end of the introduction section, the authors should be given a clear and comprehensive paragraph to show the readers how this research has been done (the main problem, contributions, experiments, comparisons, results, and so on).

I feel that the flow of the proposed method can be improved as well the mathematical notations also need to be checked and revised.

The authors should give the readers a straightforward method of choosing the experiments and their design. This will help the future researcher in this domain conduct new research and start from the current paper.

For example, the following papers might be cited in your work.

Local path planning of driverless car navigation based on jump point search method under urban environment

SMURF: A Fully Autonomous Water Surface Cleaning Robot with A Novel Coverage Path Planning Method

A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model

A survey of trajectory planning techniques for autonomous systems

Data driven model estimation for aerial vehicles: A perspective analysis

A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model

The discussion of the results needs to include the strengths and weaknesses of the proposed algorithm.

Furthermore, where are the limitations of your study? Clarifying the study's limitations allows the readers to better understand under which conditions the results should be interpreted. A clear description of the limitations of a study also shows that the researcher has a holistic understanding of his/her study. However, the authors fail to demonstrate this in their paper.

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The organization of this paper should be ehanced.

Some recent works from high-quality journals should be cited in this research.

Author Response

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Reviewer 3 Report

Section 1 contains many lumped references, but an in-depth discussion of each work should be provided instead. A focus MUST be given to the analysis of path planning specifically applied in tractors. I will give only a few examples that could be useful for this purpose:

[R1] Ljungqvist et al (2019). A Path Planning and Path‐following Control Framework for a General 2‐trailer with a Car‐like Tractor. Journal of field robotics, 36(8), 1345-1377.

[R2] Parsons et al (2023). Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor. Agriculture, 13(1), 56.

ACO is a well-known metaheuristic that has been applied to a wide variety of problems. However, possible limitations of this approach include the stagnation phase, exploration and exploitation rates, and convergence speed of the algorithm. How do such issues affect the overall performance of the tractor?

Besides, is the work focused on the simple application of an existing solution to a practical problem? If so, the contribution is minor. So, please, elaborate.

The organization of topics in section 3 is a little bit confusing. Considering that equations are presented in sections 3.1 and 3.2, they should be part of the methodology, not the results. I suggest you to merge sections 3 and 4 while creating a topic related to "Results and Discussion".

In the conclusion, the authors state the algorithm can reduce the length and energy consumption of the planned path. However, there is no evaluation of such issues unlike the study performed in [R3]. Please, elaborate.

[R3] de Melo et al (2022). Wheel Slip Control Applied to an Electric Tractor for Improving Tractive Efficiency and Reducing Energy Consumption. Sensors, 22(12), 4527.

Author Response

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Round 2

Reviewer 1 Report

The current version of the paper presents an expressive improvement as compared to the previous one. The authors provided acceptable answers to all questions and no more issues were detected in the current manuscript. Therefore, I recommend the acceptance of the paper.

Author Response

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Reviewer 2 Report

ok

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Reviewer 3 Report

The authors were given a chance to revise the manuscript. The first criticism is that the modified content was not highlighted and therefore the reviewers have no other option but to read the whole manuscript again, which is frustrating. Besides, most of my requests were simply ignored.

1) Starting from the introduction, the lumped references were NOT REMOVED. There is no reason for doing so, so it is very easy to come up with more than 30 references like the authors did. Besides, most works are useless and/or published in irrelevant journals, like [1], [2], and so on. The literature on both EV tractors and metaheuristics is vast, and I provided the authors with some suggested references published in renowned journals, which were simply ignored. Neither [4]-[9] nor [22]-[26] refer to prestigious publications.

2) The explanation given by the authors about the convergence of ACO is not convincing. Besides, it is not compared with the proposed approach to justify the application of combined approaches.

3) The combination of two existing heuristics is not well justified. Besides, WOA has been associated with other similar approaches as in [R1]. Once again, the explanation is too simplistic and not convincing.

[R1] Rana, N., Latiff, M. S. A., Abdulhamid, S. I. M., & Chiroma, H. (2020). Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Computing and Applications, 32, 16245-16277.

4) In the conclusion, the authors state the algorithm can reduce the length and energy consumption of the planned path. However, there is no evaluation of such issues unlike the study performed in [R3].

[R3] de Melo et al (2022). Wheel Slip Control Applied to an Electric Tractor for Improving Tractive Efficiency and Reducing Energy Consumption. Sensors, 22(12), 4527.

In turn, the authors claim that they supplemented the quantitative analysis of the experimental results of the three algorithms to better illustrate that the IWOA-ACO algorithm can reduce the length and energy loss of the planned path.

Firstly, there are no experimental results, only simulation ones. Secondly, the authors did not present the plots showing the energy consumption versus time as in [R3]. Another ignored request!

Author Response

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