Two-Stage Hierarchical 4D Low-Risk Trajectory Planning for Urban Air Logistics

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors The paper presents a two-stage hierarchical approach for low-risk trajectory planning of multiple UAVs in urban air logistics scenarios. The proposed method focuses on minimising risks and conflicts during UAV operations in urban Very Low-Level airspace, which is critical due to high-density UAV operations in these environments. The first stage focuses on minimising the risk of collisions and ground risks before the UAVs take off. The proposed module utilises an improved A* algorithm to plan trajectories while considering both collision and ground risks. The second stage addresses conflicts that arise during flight, which ensures that conflicts are resolved without causing secondary conflicts by incorporating Estimated Time of Arrival, particle swarm optimisation, and artificial potential field methods for real-time conflict resolution and trajectory adjustments. In general, the paper is well written and clear to follow. Here below are some comments:   - The ground risk model is a key aspect of the paper. While population density is used as the primary factor to quantify ground risk, it may not sufficiently capture all potential real-world scenarios. The model might benefit from integrating more detailed risk parameters, such as: types of land use (e.g., residential, industrial, commercial), different levels of pedestrian mobility during rush hours or special events.   - The simulation results demonstrate the effectiveness of the proposed approach in reducing risks. I think it is also important to consider how the algorithm would perform in real-world, unpredictable environments. A discussion on transitioning the proposed method in real-world applications, such as challenges in handling dynamic and uncertain environments, would improve the paper's relevance to practical applications.   - The proposed system is presented as a stand-alone trajectory planning approach, but there is little discussion on how this method could be integrated with existing UTM systems or UAM frameworks. Potential integration challenges could be discussed, such as data exchange, coordination between UAV operators, or adapting to existing regulations.   - The introduction of ETA for conflict avoidance is a key feature, but the authors may want to justify how reliable is this prediction. In the case of unforeseen delays or variations in UAV speed, due to wind conditions for instance, could this method still ensure conflict-free resolution, or could it lead to new issues? The use of machine learning to predict UAV behaviour or the introduction of adaptive strategies based on real-time feedback might enhance the robustness of the method.   - How does the proposed two-stage trajectory planning approach scale when the number of UAVs increases significantly, or when the grid size is reduced for higher map resolution? The authors may want to clarify if there is a performance bottleneck when dealing with a larger fleet of UAVs in a complex urban environment, and if so, the potential ways to address the bottleneck in future work.  

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Language and format needing reviewing. More references needed. The framework is unclear  and the extensive experiments isn't portrayed well in the paper to justify your validation

Comments on the Quality of English Language

Inconsistent throughout the paper

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Two-stage Hierarchical 4D Low-risk Trajectory Planning for Urban Air Logistics

Yuan Zheng 1, Yichao Li 2, Jie Cheng 3, Chenglong Li 2,* and Shichen Hu 4

 

Line 202 review the numeration

Line 228 improve the figure 1 it’s not clear

Line 329 improve the figure 3 it’s not clear

Line 375 improve the figure 1 it’s not center

Line 496 put MATLAB Copyright right and used version

Line 539 put the table 1 description in the same page that the table 1

Line 633 Improve Conclusion whit the numerical data obtained the error and the improvement from the experiments

References its OK

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Congratulations to the authors. The article is interesting. 
But I have a suggestion. The authors should compare results with the experiment tests. The next suggestion is that you should include the influence of the environment eg. a thermal movement of air, wind, and turbulence. The move of aircraft disturbs the atmosphere so there are interactions between UAVs. These phenomena affect trajectories. 

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Good changes into the recommendations