An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares
Round 1
Reviewer 1 Report
This is an experimental paper. The extended Kalman filter thermal center prediction method based on ordinary least squares is proposed to develop the autonomous soaring system for small drones and it is innovative to some extent. But the content needs to be promoted, specifically as follows:
1. The author should be able to write the article in the format of a research paper. Introduction, Material and Methods, Results and discussion, Conclusions. Otherwise, the logical structure of the paper is not clear.
2. Abstract is the content shorten of the paper. The author puts too much emphasis on the advantages of the article in this paragraph, the main research process and results of the article are unclear.
3. The reviewer thinks that the CFD simulation and wind direction test in Part 2 are not the main research content of this paper, and a brief description is enough.
4. Part 2 should be written together with Part 3, and the formulas and test methods should be separated from the test results.
5. The discussion is the core of the paper, and the author is missing. Test of simulation and field for autonomous soaring system should be combined, then, the author discusses the differences between them and puts forward the improvement strategy of your own research methods.
None.
Author Response
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Author Response.docx
Reviewer 2 Report
The paper titled "An autonomous soaring for small drones using the extended Kalman filter thermal updraft center prediction method based on ordinary least squares" presents a compelling exploration into the application of avian-inspired soaring behavior to enhance the performance of small drones. The authors introduce the Extended Kalman Filter (EKF) thermal center prediction method based on Ordinary Least Squares (OLS) to achieve autonomous soaring, demonstrating its effectiveness through rigorous experimentation. The study not only contributes to the field of drone autonomy but also draws inspiration from nature to address challenges associated with drone endurance and range.
A key strength of the proposed approach lies in its innovative integration of EKF and OLS for thermal center prediction. By approximating the distribution of thermal updrafts using OLS, the authors effectively alleviate the computational burden associated with predicting thermal centers. This reduction in complexity, coupled with real-time updates, represents a noteworthy achievement. The method's low computational load aligns well with the operational constraints of small drones, and the reported advantages of rapid convergence and high stability underscore its practical utility.
The incorporation of an adaptive step-size update strategy into the EKF algorithm further enhances its convergence speed. This augmentation demonstrates a clear commitment to refining the algorithm's performance and underscores the authors' commitment to optimization.
The experimental validation on the Talon fixed-wing drone platform serves as a pivotal aspect of the study. The real-time updates of the thermal updraft center at a frequency of 1Hz demonstrate the feasibility of the proposed system. The ability of the drone to successfully engage in static soaring within thermal updrafts while utilizing propulsion for only a fraction of its flight duration is particularly impressive. This outcome underscores the practicality of the autonomous soaring system and provides strong evidence of its efficacy in extending drone endurance.
In conclusion, the paper presents an innovative approach to autonomously enhance drone endurance and range through avian-inspired soaring behavior. The integration of EKF and OLS methods, along with the adaptive step-size update strategy, contributes to the method's efficiency and reliability. The experimental results validate the approach's feasibility and effectiveness, suggesting its potential to revolutionize small drone capabilities. The study opens avenues for future research in autonomous flight and bio-inspired control strategies, making a substantial contribution to the field of aerial robotics.
Author Response
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Author Response File: Author Response.docx
Reviewer 3 Report
1. Please provide more details on the implementation and experimental setup of the autonomous soaring system, including specific sensors and hardware used for sensing and control.
2. Please set up a control experiment with existing autonomous soaring methods to highlight the advantages and novelty of the proposed OLS-based EKF thermal updraft center prediction method.
3. Discuss the limitations and potential challenges of the proposed approach, such as its performance under different environmental conditions and its scalability to large UAVs or complex flight scenarios.
4. Discuss the convergence speed of the algorithm and its effectiveness in thermal updraft center prediction, especially when the wind field is weak. Provide a visual representation, such as a convergence curve, to demonstrate rapid convergence to the true thermal updraft center point.
5. Consider including a section on the validation and evaluation of the autonomous soaring system, including quantitative metrics and comparisons with baseline methods or simulations.
6. Some existing literature should be added to further analyze and discuss the contribution of this paper, like, Containment Tracking for Networked Agents Subject to Nonuniform Communication Delays, Robust bipartite tracking consensus of multi-agent systems via neural network combined with extended high-gain observer.
It is recommended that the authors should check the language carefully, so as to further improve the quality of the manuscript.
Author Response
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Author Response File: Author Response.docx
Reviewer 4 Report
The article is interesting, but some concerns are
1. How these activities have been decided?
2. What is probabilistic model behind these activities?
3. It is unclear that what is impact of the place mentioned in activities?
4. What is the contribution of all the authors?
5. How the methodology predict better, comparison with existing need to mention?
6. Results are not convincing.
7. A Related Works section should need improvisation, and the Introduction section should be rewritten.
8. It needs a discussion section.
Article need extensive english correction.
Author Response
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Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
None.
Reviewer 3 Report
I have no further comments！
