Optimality and Robustness in Multi-Robot Path Planning with Temporal Logic Constraints
Bibliographic record
Abstract
In this paper we present a method for automatic planning of optimal paths for a group of robots that satisfy a common high-level mission specification. The motion of each robot is modeled as a weighted transition system, and the mission is given as a linear temporal logic (LTL) formula over a set of propositions satisfied at the regions of the environment. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize a cost function that captures the maximum time between successive satisfactions of the optimizing proposition while guaranteeing that the formula is satisfied. When the robots can follow a given trajectory exactly, our method computes a set of optimal satisfying paths that minimize the cost function and satisfy the LTL formula. However, if the traveling times of the robots are uncertain, then the robots may not be able to follow a given trajectory exactly, possibly violating the LTL formula during deployment. We handle such cases by leveraging the communication capabilities of the robots to guarantee correctness during deployment and provide bounds on the deviation from the optimal values. We implement and experimentally evaluate our method for various persistent surveillance tasks in a road network environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".