A language for robot path planning in discrete environments: The TSP with Boolean satisfiability constraints
Bibliographic record
Abstract
In this paper we introduce a new language in which discrete path planning problems for mobile robots can be specified and solved. Given an environment represented as a graph and a Boolean variable for each vertex to represent its inclusion/exclusion on the path, we consider the problem of finding the shortest path (or tour) in the graph subject to a Boolean satisfiability (Sat) formula defined over the vertex variables. We call this problem Sat-Tsp. We show the expressiveness of this language for specifying complex motion planning objectives in a discrete environment. We then present three solution techniques for this problem, including a novel reduction to the well known travelling salesman problem (Tsp). We present extensive simulation results which compare the performance of the three solvers on standard benchmarks from Tsp, Sat, and Generalized Tsp (Gtsp) literature.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".