HMLPA*: a hierarchical multi-target LPA* pathfinding algorithm designed for dynamic indoor path network
Bibliographic record
Abstract
The Lifelong Planning A* (LPA*) algorithm demonstrates unique advantages in dynamic pathfinding by employing incremental update techniques that reuse previously computed search results, enabling rapid path replanning in dynamic road network environments. However, when changes occur near the starting point or specific positions, such as key intersections, bottlenecks, or locations with high connectivity within the path network, even minor changes can trigger significant adjustments, which significantly increase the re-routing search space and computational costs of LPA*. To address this limitation, we propose a Hierarchical Multi-target LPA* (HMLPA*) algorithm that partitions the indoor path network into multiple subgraphs using the METIS graph partitioning algorithm and constructs an abstract trunk graph based on the key nodes of these subgraphs, thereby forming a hierarchical structure for the indoor path network. By leveraging this hierarchical structure, The HMLPA*’s subalgorithm, Multi-target LPA* (MLPA*), initiates pathfinding and confines re-routing to affected subgraphs and the abstract trunk graph. This localized re-routing approach effectively limits the search scope and significantly reduces computational overhead. Experimental results demonstrate that HMLPA* substantially outperforms LPA* in rerouting efficiency, effectively mitigating the high computational costs associated with the dynamic computational environment of the indoor path network.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".