Passivity-Based Connectivity Maintenance of Teleoperated Multi-Robots Under DoS Attacks
Why this work is in the frame
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Bibliographic record
Abstract
Teleoperated multi-robots rely on communications, both between the human’s local robot and the multiple remote robots and among the remote robots themselves, to execute the remote tasks commanded by their human operator. If attacked, the communications may lead to task failure, loss of multi-robot connectivity and possibly unstable teleoperation. To render teleoperated multi-robots resilient to Denial of Service (DoS) attacks with arbitrary frequency and duration, this paper augments a passivity-based controller for normal teleoperation with: 1) a controller that stops the remote robots at safe distances from each other and from obstacles when a DoS attack starts; and 2) a controller that restores the multi-robot connectivity before resuming normal teleoperation when a DoS attack stops. The teleoperation of a simulated multi-robot system with one leader and two followers illustrates the effectiveness of the proposed distributed control strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Research and industry increasingly seek to use robots in inaccessible unstructured environments Teleoperated multi-robots are ideally suited for such environments because they fuse human cognition with remote multi-robotic execution. However, they can be hindered by cyber attacks on their communications. As cyber attacks become more prevalent, resilience to them becomes increasingly important for practical teleoperated multi-robots. This paper presents a first distributed control strategy for rendering teleoperated multi-robots resilient to DoS attacks. For industrial practitioners, the proposed strategy has two key advantages: 1) it is straightforward to implement; and 2) it is effective for DoS attacks with arbitrary frequency and duration. Future work will tackle teleoperated multi-robots under malicious attacks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it