On Minimum Time Multi-Robot Planning with Guarantees on the Total Collected Reward
Bibliographic record
Abstract
In this paper, we study a multi-robot planning problem where a team of robots visit locations in an environment to collect a specified amount of reward in the minimum possible time. Each location has a weight associated with it that represents the value or amount of reward that can be collected by visiting that location. The problem is to design tours for the robots to collect at least D units of reward while minimizing the length of the longest robot tour. The single robot unweighted version of this problem is called the k-STROLL problem. We provide a 3-approximation algorithm for the multiple robot version of the k-STROLL problem. This leads to an algorithm for the weighted problem that collects at least (1 - ϵ)D reward with tour lengths of at most 3 times the optimal tour length. The analysis of the approximation algorithm is then extended to provide bi-criterion approximations to two variations of the problem. We provide an application of the approximation algorithm for planning UAV tours with gimballed cameras for monitoring an urban environment.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".