Comments

Abstract and Introduction

Enabling technology for achieving resilience to both partial and complete dropouts of localisation of individual vehicles in large teams.

First section: System design resists partial localisation dropouts of individual robots or subgroups because of unavailability of localisation modalities.

Distributed state estimator architecture. Grounded in modeling movements of surrounding fog agents. Their estimated positions serve as focal localisation anchors.

Second section: SOTA techniques aimed at achieving resilience in global localisation dropout. Relies exclusively on relative onboard measurements.

Onboard mutual perception system to determine relative position of neighbouring agents, used to define unambiguous reference frame within local constellation.

Proposed Research Scope

Show that robot swarm adapts more effectively to localisation dropouts than single agents.

Overall Evaluation

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

2: Accept

Paper Strength

Provide detailed review, including justification of scores.

The premise of the paper is clear and succinct. The target use case is for robust localisation in case of GNSS denied situations while relying on the swarm the robot is a member of. The use case is relevant in modern times and is a highly researched field, and the use of UVDAR and swarm-based relative positioning and correction is a novel concept and also state-of-the-art.

The paper refers to previous articles published by authors that delve into the control theory aspects of the implementation. This offers more insight into the robust state estimator and controller used in this application.

The paper offers sufficient and descriptive visualisation that summarise the findings and conclusions succinctly. The inclusion of videos to elaborate on this further is appreciated.

The experimentation and validation of theory is well documented and follows scientific rigour and principles of repeatability.

The future works and conclusion section express the author's interest to test their methodology on large, real-life swarms to validate the claims of the paper: increased robustness in estimation with larger swarms, true decentralisation and scalability, responsiveness and adaptiveness to changing environments and scenarios.

Furthermore, there is an intention to introduce new technologies into the mix such as AI for threat detection and mitigation, and further generalisation to accommodate heterogeneous swarms. However, no rough plan of action or conceptualisation of approach is described.

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.

The extended abstract encapsulates a large breadth of content. There would be some value in splitting it up into two sections: (1) to emphasise and elaborate on the problem statement while also briefly addressing relevant literature, (2) to describe the proposed solution and strategy.

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

2: Well Aligned

The proposed approach to implement the solution with drones and drone swarms aligns with SSRC's current trajectory to explore and secure the UAV and UAV swarm landscape.

It also relies on technologies that are relevant and state-of-the-art, which also aligns with SSRC's approach to explore and augment up an coming technologies.

There is scope for integrating the proposed solution into SSRC's current sub-focus of system recovery to adverse scenarios (along the lines of run-time assurance, system recovery, etc.).

The future goal of the project also aims to integrate the solution into a fully zero-trust architecture, which is what SSRC is aiming to implement in drone swarms.

One limitation is that the application is tethered to a very specific use and application case. Which means that it is not a transformable solution to other system designs.

One problem that is anticipated is the thin line the project walks between system controls and system security. Designing controllers for drone swarm isn't aligned with SSRC's research and is more attuned to ARRC. However, the paper is presented as validation of a functioning and pre-designed control scheme. It is now matter of integration, which is sufficiently separated from ARRC's research interests.

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

Justify answer.

Weak accept. The scope provided in the paper appear narrow at first glance. For a two year project, the direct application and implementation of the fully GNSS denied localisation strategy is estimated to be a short endeavour. It is a matter of integrating the algorithm into existing architecture.

However, should the project scope extend into the suggested future works, such as incorporation of AI algorithms for threat detection, and design of coordinated mechanisms to enable collective swarm response to threats, then there is scope for a two year project.

Furthermore, some clarity is necessary in defining the project scope should this be taken on, so that there is evident separation from ARRC's research interests.

Confidential remarks for the program committee.

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