Resilient and Efficient Multirobot Pickup and Delivery Against Strategic Attacks: A Three-Layered Framework
Bibliographic record
Abstract
The widespread application of intelligent storage systems has urged more requirements for system security. In this work, we consider a resilient pickup and delivery task of the multi-robot systems against strategic attacks. The strategic attacks terminate the normal motion of a fraction of key operating robots with a well-designed strategy, and thus jeopardize the cooperation among robots. A three-layered decision framework is proposed to suppress the above strategic attacks: The first layer introduces defensive strategies, which incorporate reputation mechanisms and counterfactual rescue mode allocations. The counterfactual rescue-mode allocation mechanism dynamically assesses the benefit differences between rescuing others for resilience and conducting self-tasks for efficiency. Based on the reputation mechanisms, the second layer employs a bi-level programming method for pickup/delivery mode allocations and task assignments. Then, the third layer calculates the collision-free path for the swarm using a mixed integer linear programming model. The practicality and resilience of this algorithm against strategic attacks have been demonstrated through numerical simulations involving various robot scales and task burdens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".