A graph-based approach to multi-robot rendezvous for recharging in persistent tasks
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Bibliographic record
Abstract
This paper addresses the problem of maintaining persistence in coordinated tasks performed by a team of autonomous robots. We introduce a dedicated team of charging robots to service a team of primary working robots. Given that the trajectories of the working robots are known within a planning interval, the objective is to plan routes for the charging robots such that they rendezvous with and recharge all working robots to guarantee their continuous operation. To this end, the working robot trajectories are discretized to form a finite set of recharging points at which rendezvous can occur. The problem is formulated as a directed acyclic graph with vertex partitions containing sets of charging points for each working robot. Solutions consist of paths through the graph for each of the charging robots. The problem is shown to be NP-hard and a mixed integer linear program formulation is presented and solved for small problem instances. Finally, it is shown that while the optimal solution is not computationally feasible for large problem sizes, it is possible to graphically transform the single charging robot problem to a Traveling Salesman Problem, for which existing heuristic and approximation algorithms can be applied. Simulation results are presented for both single and multiple charging robot scenarios.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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