Demand-Oriented Autonomous Navigation for Mobile Robots With Vector-Semantic Mapping in Urban Environments
Bibliographic record
Abstract
Autonomous navigation of mobile robots in urban environments is crucial for independent travel for the elderly or disabled populations. However, existing robot urban navigation solutions usually rely on the precise coordinates of the target location, and do not comply with traffic rules during navigation. To this end, this paper proposes a demand-oriented robot navigation solution in urban environments based on vector-semantic map (V-S Map), where V-S Map integrates geometrically precise vector layer generated using a Transformer architecture, and task-related semantic layer derived via optical character recognition and point cloud registration. Unlike current mapping approaches, V-S map uniquely incorporates directional road attributes and task-relevant semantic information to address traffic violations in urban navigation while establishing the relevance of tasks to the environment. Furthermore, we develop an LLM-based command parsing strategy that combines optimized prompt engineering with regular expression matching against location lexicons extracted from semantic layer. This strategy eliminates traditional coordinate-input constraints, enhancing human-robot natural language interaction capabilities. Extensive experiments in simulation and real-world environments verify the effectiveness and feasibility of the proposed solution in terms of ambiguous command parsing and robot navigation performance, achieving an 80% overall task success rate with a 63.78% path-violation weighted success rate.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".