Optimal collision free path planning for an autonomous articulated vehicle with two trailers
Bibliographic record
Abstract
This paper presents a motion planning algorithm for generating optimal collision-free paths for robotic vehicle with two trailers moving autonomously. The proposed algorithm is based on combination between artificial potential field method (APF) and optimal control theory. The optimal control theory is applied to generate an optimal collision-free path for robotic vehicle from a starting point to the goal point. On the other hand, the proposed APF is based on two-dimensional Gaussian function to represent goal location as attractor and obstacles as repulsors and consequently, will control the steering angle of the robotic vehicle so that it can reach to its target location safely avoiding collision. A linear two-degree-of-freedom vehicle model with linear tire characteristics is derived to represent the vehicle motion considering the lateral and yaw dynamics. Several simulations are carried out to check the fidelity of the proposed technique and the illustrated results demonstrated the generated path for the robotic vehicle with two trailers satisfy vehicle dynamics constraints, avoid collision with the obstacles and reach the target location safely. The simulations results demonstrated the efficiency of the proposed algorithm and its success in dealing with complex environments with different obstacles.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".