IQ-RRT*: a path planning algorithm based on informed-RRT* and quick-RRT*
Bibliographic record
Abstract
Optimal path planning algorithms such as the RRT* and its variants seek to generate the best feasible path from an initial state to a goal state in the least possible time. Prior work on RRT* has focused on improving the convergence rate of the algorithm while keeping its computational complexity unchanged. Informed-RRT* and quick-RRT* are two such variants that, in certain scenarios, converge to the optimal path faster than RRT* does. This work focuses on the novel addition of informed sampling to quick-RRT* to enhance its convergence rate. The resultant algorithm provides initial solutions with costs comparable to quick-RRT* and convergence rates at par with quick-RRT* in the worst case. The authors have concluded that this new algorithm, named IQ-RRT*, outperforms informed-RRT* and quick-RRT* in a multitude of scenarios. IQ-RRT*, unlike quick-RRT*, is a faster alternative to informed-RRT* even in cluttered environments and mazes with long corridors.
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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 | 0.001 | 0.004 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".