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The paper can be accepted. It was well improved. Even one reviewer rejected the paper has a good value.
Regarding the Figure 4, 5 and 11, the font of the flowchart is too small. If you can adjust the font size, it would be better in terms of resolution.
Sufficient
Sufficient
All of the comments have been addressed and have been improved.
I propose major revision since the reviewers offered many suggestions, so authors can work on a better version of the paper.
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While the manuscript (Enhanced indoor pathfinding navigation in multistoried buildings using a hybrid of RRT-connect and Dijkstra’s algorithm) presents a theoretically sound approach to indoor navigation, it fails to offer sufficient novelty, real-world validation, or comprehensive evaluation to be considered for publication. The authors should conduct more thorough testing, provide deeper algorithmic explanations, and clearly highlight the contributions and limitations of their work. Based on these concerns, I recommend rejecting the manuscript in its current form.
Comment 1: The manuscript does not offer a sufficiently novel or innovative contribution to the domain of indoor navigation and pathfinding. Similar algorithms and methodologies, including RRT and Dijkstra’s, have been thoroughly examined in the current literature. The integration of RRT-Connect with Dijkstra's, although a legitimate strategy, fails to provide substantial improvement over current methods in a significant or innovative manner. The authors need to demonstrate more clearly how their work advances the state-of-the-art or offers unique improvements that haven't been explored before.
Comment 2: The manuscript lacks substantial real-world validation or testing. Although the theoretical framework of the algorithm is presented, there is a lack of empirical information regarding its performance in real-world settings, which is essential for demonstrating its practical usefulness. Simulations without real-world validation fail to demonstrate the effectiveness of the system in dynamic or unpredictable real-world scenarios. The authors should conduct a field experiments in actual buildings, potentially incorporating dynamic barriers and user interactions, to demonstrate the system's true performance.
Comment 3: There is a noticeable absence of comprehensive benchmarking results. The manuscript's comparison of RRT-Connect with algorithms such as RRT and Dijkstra’s the comparison is too superficial. It lacks detailed metrics such as computational time, memory usage, or robustness in complex scenarios (e.g., dense obstacles, large-scale buildings).
Comment 4: The explanation of the hybrid algorithm using RRT-Connect and Dijkstra’s is unclear and lacks depth. While the concepts are mentioned, the paper does not provide enough detailed reasoning behind the design choices or how these algorithms work together in a hybrid manner. Key aspects of the system, like how obstacles are detected and how the path is dynamically recalculated, are glossed over.
Comment 5: The manuscript lacks significant discourse on the scalability of the method or its applicability to more complex environment. It concentrates on multi-story structures but fails to include the system's management of extensive or expansive internal areas, as well as multi-building complexes.
Comment 6: The paper references the creation of a user interface utilizing React but offers few information regarding its practical functionality. The interaction of users with the system during navigation and its adaptation to evolving environments is not addressed. However, A more detailed explanation of the UI design and its interaction with the pathfinding system is needed, especially how the UI handles real-time updates and obstacle changes.
Comment 7: The manuscript insufficiently discusses the limitations of the suggested methodology and the potential obstacles it may encounter in practical implementation. Critical difficulties such as managing extreme environmental conditions, dynamic alterations in the building structure, or user related challenges are not addressed. A more thorough examination of these potential limitations would provide a more realistic picture of the system’s applicability in real-world scenarios.
Language and Clarity:
The paper demonstrates a good understanding of professional English but contains several typographical errors, such as "reûning" instead of "refining" and "pathûnding" instead of "pathfinding." Ensure these are corrected throughout the document.
Some sentences are overly complex, such as those in the introduction and methodology sections. For example, "By combining the rapid tree growth of RRT-Connect with the systematic, shortest-path assurance of Dijkstra’s algorithm..." could be broken down for better readability.
Literature References:
Some citations, like those for "Singh et al. (2024)" and "Zhang et al. (2021)," are not adequately integrated into the discussion. Clearly explain the relevance of these works to the research question.
Structure and Figures:
The figures lack clarity and readability. For example:
The font size in flowcharts is too small, and some diagrams appear pixelated (e.g., Figures 5 and 7). Use higher-resolution images and larger fonts.
Graphs need proper axis labels and legends. For instance, Figures 9 and 10 should specify the units for computation time and explain the test scenarios.
Tables, such as Table 1, are basic and lack emphasis on key insights. Use bold or color-coded text to highlight important comparisons.
Raw Data:
While the methodology provides detailed descriptions, it is unclear if raw data (e.g., the spatial database or performance metrics) is accessible for replication. Consider including a dataset link or appendix with raw data for transparency.
Definitions and Formal Results:
Some technical terms (e.g., "spatial database," "CPU parallelization") are not defined in detail. Providing clear definitions and examples would enhance understanding.
The formal results section is strong but would benefit from a clearer summary of how each hypothesis or research question was addressed.
Originality and Scope:
The study is original and aligns with the journal's scope. The hybrid approach of combining RRT-Connect and Dijkstra’s algorithm is innovative and meaningful for indoor navigation.
Research Question:
The research question is well-defined, but the paper could emphasize how the proposed method addresses specific gaps in existing literature. For example, highlight how it compares to RRT* or other modern algorithms in greater detail.
Methodology Details:
While the methodology is described in detail, some areas require clarification for replication:
Hardware Specifications: The parallel processing section does not specify the hardware used (e.g., CPU model, core count). This information is critical for reproducibility.
Algorithm Parameters: Key parameters such as the step size (ε), threshold (δ), and maximum iterations for RRT-Connect are not explicitly stated. These should be clearly documented.
Environment Setup: The description of test environments (e.g., narrow passages, multi-story buildings) lacks specifics. Provide diagrams or schematics of the simulated test cases.
Technical and Ethical Standards:
The paper adheres to high technical standards but could mention ethical considerations, such as the applicability of this research to real-world scenarios or its limitations in dynamic, human-occupied environments.
Impact and Replication:
The findings are significant, particularly the demonstrated efficiency of RRT-Connect in reducing computation time. However:
Include statistical significance testing or confidence intervals for results (e.g., Figures 9 and 10). This would strengthen the robustness of the findings.
Provide a clear rationale for replicating the study in other environments (e.g., different types of buildings or larger datasets).
Underlying Data:
While the results are well-presented, it is unclear if the underlying data (e.g., node exploration data, spatial database) is available for verification. Consider including supplemental material or providing a link to the dataset.
Conclusions:
The conclusions are logical and aligned with the research question but could emphasize the broader applicability of the findings. For example, discuss potential use cases such as disaster response or autonomous indoor robots.
Address limitations, such as the scalability of the approach to highly dynamic environments with moving obstacles.
General Writing Style:
The writing is professional but occasionally verbose. Simplify sentences for better readability and accessibility to a broader audience.
Avoid repetition in the methodology and results sections. For example, the description of RRT-Connect appears multiple times and could be consolidated.
Figures and Tables:
Improve figure clarity and ensure all elements are labeled and described in detail. For example, provide a legend for Figure 11 and clarify the meaning of explored node paths.
Future Directions:
The paper briefly mentions potential future improvements, such as handling dynamic obstacles. Expanding on these suggestions with specific research proposals would enhance the conclusion.
Technical Errors:
Address typographical issues and inconsistencies, such as the use of special characters (e.g., û and 9s). Perform a thorough proofreading pass to eliminate such errors.
Summary of Corrections Needed
1. Correct typographical and grammatical errors.
2. Improve figure resolution, labeling, and clarity.
3. Define key terms and provide detailed algorithm parameters for replication.
4. Consolidate repetitive content and simplify sentence structures.
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