Real-time path planning and following for nonholonomic unmanned ground vehicles
Bibliographic record
Abstract
This paper describes a computationally cost-effective path planning method by combining a hybrid A∗ path planner with potential fields. The proposed real-time path planner is capable of finding the optimal, collision-free path for a non-holonomic unmanned ground vehicle (UGV) in an unstructured environment. First, a hybrid A∗path planner is designed to find the optimal path through connecting the current position of the UGV to the target in real-time while avoiding any obstacles in the vicinity of UGV. The advantages of the developed path planner are that, by using the potential field techniques and by excluding the nodes surrounding every obstacles, it significantly reduces the search space of the traditional A∗approach; it is also capable of distinguishing different types of obstacles by giving them distinct priorities based on their natures and safety concerns. Such an approach is essential to guarantee a safe navigation in the environment where humans are in close contact with autonomous vehicles. Then, with consideration of the kinematic constraints of the UGV, a smooth and drivable geometric path is generated. Finally, extensive practical experiments are conducted in a dynamic environment to verify the effectiveness of the proposed path planning methodology.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".