Decentralized Energy-Aware Co-Planning of Motion and Communication Strategies for Networked Mobile Robots
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Bibliographic record
Abstract
In this article, a decentralized planning scheme is proposed to determine simultaneously communication and motion strategies for a team of mobile robots. These robots accomplish a collection of target visiting tasks in a complex environment with optimal energy consumption and guaranteed end-to-end connectivity. Information generated during the team deployment is transmitted to an operation center via a multihop wireless network whose channels are modeled by stochastic variables. For each announced task, mobile robots adopt different roles depending on the task's nature and the team's current configuration; then, each robot determines its communication and motion policies by solving a convex optimization problem. Avoiding inter-robot collisions and obstacles is also taken into account. The suggested approach leads to the efficient use of available robots and their energy resources compared to the rival methods in the literature. Effectiveness of the proposed algorithm is illustrated by computer simulations.
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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 | 0.000 |
| 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