Optimal path planning for an autonomous articulated vehicle with two trailers
Bibliographic record
Abstract
This paper proposed an optimal path planning algorithm for autonomous vehicle with two trailers in autonomous navigation. The proposed algorithm is based on combination of artificial potential field (APF) method and optimal control theory. A linear two-degree-of-freedom vehicle model with both lateral and yaw motion is derived and simulated in MATLAB environment. The optimal control theory is applied to generate an optimal free-obstacle path of the robotic vehicle from a starting point to the goal location. The obstacle-avoidance technique is mathematically modelled using a potential function based on the proposed sigmoid function. The constructed potential field model can achieve an accurate analytic description of objects in three dimensions. Moreover, the proposed model of potential field requires very modest computation at run time. The APF includes both the attractive (the target) and repulsive (the obstacles) potential fields that will control the steering angle of the vehicle so that it can reach to its target location. Several simulations are carried out to check the fidelity of the proposed technique. The illustrated results demonstrate the generated optimal path of autonomous vehicles with consideration of vehicle dynamics constraints, obstacle avoidance and collision free criteria in reaching the goal location.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".