Passivity-Based Connectivity Maintenance of Teleoperated Multi-Robots Under DoS Attacks
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.Note to Practitioners—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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".