Randomized path planning with preferences in highly complex dynamic environments
Bibliographic record
Abstract
SUMMARY In this paper we consider the problem of planning paths for articulated bodies operating in workplaces containing obstacles and regions with preferences expressed as degrees of desirability. Degrees of desirability could specify danger zones and desire zones. A planned path should not collide with the obstacles and should maximize the degrees of desirability. Region desirability can also convey search-control strategies guiding the exploration of the search space. To handle desirability specifications, we introduce the notion of flexible probabilistic roadmap (flexible PRM) as an extension of the traditional PRM. Each edge in a flexible PRM is assigned a desirability degree. We show that flexible PRM planning can be achieved very efficiently with a simple sampling strategy of the configuration space defined as a trade-off between a traditional sampling oriented toward coverage of the configuration space and a heuristic optimization of the path desirability degree. For path planning problems in dynamic environments, where obstacles and region desirability can change in real time, we use dynamic and anytime search exploration strategies. The dynamic strategy allows the planner to replan efficiently by exploiting results from previous planning phases. The anytime strategy starts with a quickly computed path with a potentially low desirability degree which is then incrementally improved depending on the available planning time.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".