Comments

Abstract and Introduction

Approach leverages power of LLMs (lightweight) running on the edge to enable autonomous drone swarms to execute high level commands through natural language interaction.

Proposed Research Scope

Four (4) Key components:

1. Speech-to-Text conversion
2. Objective Understanding and Planning
3. Object Detection and Annotation
4. Plan Execution and Feedback

Focused on using very lightweight architectures and models, s.t. it can be deployed on resource-constrained embedded devices like Nvidia Jetson Nano or Raspberry Pi.

Cost-effectiveness and accessibility are also taken into consideration (no Lidar or expensive sensors).

System Architecture

Hybrid approach where combination of central LLM handling high-level planning and coordination with distributed lightweight LLMs on each drone to manage local perception and plan refinement.

Overall Evaluation

2: accept; 1: weak accept; 0: borderline accept; -1: weak reject; -2: reject

-2: Reject

Paper Strength

Provide detailed review, including justification of scores.

The authors have conducted sufficiently thorough literature review on the topic to identify gaps in existing works and propose an approach that fills in the gap.

However, the paper could use more work to address its shortcomings (see next section).

Suggestion to Improve

Provide suggestions that could help improve the paper. Consider commenting on areas such as clarity, methodology, data analysis, relevance of literature reveiw and overall presentation.

There is room for improvement in the presentation, clarity of methodology, presentation of data analysis.

Figures and tables need to be cross-referenced and elaborated upon in the body. Furthermore, the quality of figures used could use some improvement. The real-world experiment setup is illegible and hard to understand.

There is a heading "3.1.2 Comparison of Architecture" without a body.

The experiment and result sections is bare-bone and cannot sufficiently justify the work that has been conducted. This could use some elaboration maybe by trimming some of the precursor sections. There is a lack of quantitative results; Table 2 is hard to interpret without description.

Experiments themselves required more expounding on the methodology (scenario, method, purpose).

Finally, some justification on why specifically LLMs need to be used is necessary. The authors express in the introduction that the approach is proposed to address the challenge of "efficient decision-making" to accomplish "high-level" objectives, but why specifically use LLMs for this?

Is this the right way to go? Was the purpose simply a matter of exploration? In which case it would be a good idea to evaluate if the proposed novel approach is superior to existing strategies.

Relevance to SSRC/TII Research

Assess how the submission aligns with the current or future research interests of SSRC and TII.
4: Not Aligned; 3: Neutral; 2: Well Aligned

4: Not Aligned

As it stands, it does not offer anything tangible that can align with current or future SSRC/TII research interests.

Would you suggest this paper for a two year project with SSRC?

Justify answer.

Not at its current state.

Confidential remarks for the program committee.

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