Research on Path Planning Algorithm Based on Fast Target Detection
Bibliographic record
Abstract
As a key technology of robot navigation, path planning has garnered widespread attention and has been utilized in various applications such as mobile robots, unmanned aerial vehicles, and human-computer interaction. Recently, several studies advocate constructing semantic maps for path planning in the laboratory stage. However, these approaches require large storage space and high computing resource consumption, making it difficult to meet real-time requirements. We tackle this issue by building real-time semantic navigation map and propose a real-time path planning algorithm based on fast target detection. Specially, we first construct two-dimensional grid map using the Gamapping method and locate the target objection utilizing the object detection algorithm YOLOv3 retrained in an indoor experimental environment. Furthermore, by incorporating the category information and position information of the detected object into the two-dimensional grid map through a coordinate mapping mechanism, we combine the geometric metric information and visual detection information to build semantic navigation map for automatically planning a reasonable path. The experiments conducted on both qualitative and quantitative levels have demonstrated that our method achieves superior performance and practical application value.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".