Research on Path Planning Algorithm Based on Fast Target Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it