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Dear Authors,
Thank you for addressing the reviewers' comments. Your manuscript now seems sufficiently improved for publication.
Best wishes,
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
Thanks for fixing the comments. The review comments look good.
Thanks for fixing the comments. The review comments look good.
Thanks for fixing the comments. The review comments look good.
Thanks for fixing the comments. The review comments look good.
All comments are in the last section.
All comments are in the last section.
All comments are in the last section.
Thank you for the revision. The responses to the reviewers’ comments and the corresponding changes to the manuscript are, for the most part, satisfactory.
Dear Authors,
We would like to express our gratitude for the submission of your manuscript. The reviewers have provided their feedback, which is now available for your perusal. It is not recommended that your article be published in its current format. However, it is strongly advised that you address the issues raised by the reviewers and resubmit your paper after making the necessary changes.
Best wishes,
The manuscript is well-structured and follows professional conventions.
Figures are relevant but could be refined for clarity.
Some minor grammatical improvements and consistency in terminology are needed.
The methodology is well thought out, but additional baselines and validation against real-world data would strengthen the study.
The experimental setup could benefit from a broader range of test cases.
The findings align with the research objectives and are well presented.
More statistical rigor (e.g., confidence intervals) would enhance credibility.
The discussion on trade-offs in Pareto-optimal solutions is insightful but could be more detailed.
Bi-objective trail-planning for a robot team orienteering in a hazardous environment – 110066
This article presents a well-structured and impactful approach to bi-objective trail planning for robotic teams, addressing critical challenges in hazardous environments with practical optimization techniques. Its combination of risk-aware navigation and Pareto-optimal decision-making makes it a valuable contribution to autonomous robotics research.
Refer below for review comments
1. Abstract
The abstract formulates a well-established reason for the work but is a little lengthy. It is best to make a concise statement about explaining the problem and its proposed solution.
It is important for the abstract to mention the key obstacles in attaining bi-objective optimization in hazardous environments. Thanks for the article.
2. Introduction
Discussion about preceding work is a somewhat inadequate. Comparisons between proposed and state-of-the-art approaches in multi-robot path planning and danger-aware path planning must be included in the manuscript. Mentioning additional multi-agent planning frameworks, specifically ones concerned with safe constraints in hostile or uncertain environments, will add value.
3. Methodology
Ant colony optimization is an interesting application but needs an explicit justification. What motivated choosing this over - reinforcement learning, or other heuristic approaches?
Can you add step-by-step narrative for replicability.
You may need to have more formal introduction for definitions of reward function and survival probability, possibly with use of equations for added clarity.
4. Experimental Setup
The case study in an art museum, its generalizability to real environments with danger / hazardous environment needs to be explained in more details. Adding an additional experiment influencing / changing environment factors (e.g., changing danger distributions and larger map scales) will contribute towards supporting resulting conclusions.
You may need to add a comparative performance evaluation. Consider adding a baseline for comparison with at least one competing state-of-the-art algorithm with current approach.
Optimization algorithm's sensitivity with regard to variation in heuristic weights (e.g., in terms of survival probability and reward trade-offs) will add value to the findings.
5. Results and Discussion
The idea of Pareto-optimums is great, providing additional information regarding selection of specific trade-offs between them for decision-makers will help readers.
Despite a bias towards use of qualitative analysis in discussion, added statistics such as confidence interval and variance estimation will help readers.
6. Conclusion
Conclusion is useful, need to add even more specific future work directions. How can work enable planning adaptability in changing environments?
Can you expand this - "A human decision-maker can then select trail plans that balance, according to their values, reward and robot survival." with an example of a real-world scenario where this decision framework would be particularly valuable.
7. Novelty and Relevance
The article must explicitly state how it advances state-of-the-art over existing risk-aware path planning algorithms.
Does this approach have ability to generalize - Can it have a chance to adapt for use in other types of mobile robots (e.g., underwater and aerial robots, such as drones)? Brief discussion in the conclusion section will help conveying the importance of this research.
All comments have been added in detail to the last section.
All comments have been added in detail to the last section.
All comments have been added in detail to the last section.
Review Report for PeerJ Computer Science
(Bi-objective trail-planning for a robot team orienteering in a hazardous environment)
1. Within the scope of the study, information-gathering mission in a museum was illustrated and ant colony optimization was implemented, bi-objective trail-planning was performed.
2. In the introduction, the importance of the subject with mobile robots orienteering and team of mobile robot applications and the main contributions of the study were mentioned. The contributions of the study were explained clearly and sufficiently. However, the literature review definitely needs to be detailed.
3. The BOTHE problem expressed regarding Bi-Objective robot-Team Orienteering was clearly expressed in terms of lower-risk and higher-risk and hazard and reward.
4. It was observed that ant colony optimization was used in the study. Although there are many different optimizations that can be used in the literature regarding the solution of this problem, it should be stated more clearly why ant colony was chosen.
5. When the results are examined in detail, an acceptable level is observed. In addition, the future work section is also clearly stated.
As a result, the study can contribute to the literature and the sections listed above should be taken into consideration.
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