Local and Nonlocal Human-to-Robot Task Allocation in Fiber-Wireless Multi-Robot Networks
Bibliographic record
Abstract
Integrated fiber-wireless (FiWi) multi-robot networks will play a pivotal role in ensuring QoS for several human-to-robot (H2R) applications due to their coverage and capacity advantages. For the successful deployment of H2R applications, efficient task allocation among robots is essential, which has emerged as an interesting research topic by taking into account a wide variety of task and robot types, task location, robot availability, capability, and failure during task execution. To render the task allocation process more efficient, we propose a task allocation scheme for FiWi-based multi-robot networks according to several key design parameters such as the availability, skill set, distance to task location, and remaining energy of robots. Furthermore, to reduce failures during task execution, we introduce a neighboring robot-assisted failure reporting mechanism. We develop an analytical model to evaluate the network performance in terms of throughput, task allocation delay, execution time, and residual energy. In addition, we analyze the end-to-end delay performance for both local and nonlocal task allocation in integrated FiWi multi-robot networks. Our results show that minimum execution time-based selection outperforms traditional minimum distance and priority-based selection in terms of total task execution time and average residual energy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".