Local and Nonlocal Human-to-Robot Task Allocation in Fiber-Wireless Multi-Robot Networks
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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