Real-Time Navigation of Nonholonomic Mobile Robots under Velocity Vector Control
Bibliographic record
Abstract
In this paper, linear navigation law is studied in depth and we suggest an efficient, practical and simple approach for nonholonomic mobile robot navigation under velocity vector control based on the linear navigation law. First of all, an obstacle is equivalent to a velocity vector when detected by a robot's sensory system according to the relative distant and relative direction between the robot and the obstacle. Then the vector sum of all obstacles' equivalent velocity vectors (OEVVs) and the linear navigation velocity vector (LNVV) derived from the linear navigation law drives the robot to reach the desired goal position without colliding with any obstacle in the robot's workspace. Furthermore, during the process of driving the mobile robot under the resultant velocity vector, a set of strategies for velocity and acceleration constraints (VAC) is devised to make kinematic behaviours of the mobile robot more practical. Finally, to validate the effectiveness and superiority, extensive simulation results with no obstacles, a single obstacle and multiple obstacles are provided.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".