Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle
Bibliographic record
Abstract
This paper presents an optimal collision-free path planning algorithm of an autonomous multi-wheeled combat vehicle using optimal control theory and artificial potential field function (APF). The optimal path of the autonomous vehicle between a given starting and goal points is generated by an optimal path planning algorithm. The cost function of the path planning is solved together with vehicle dynamics equations to satisfy the vehicle dynamics constraints and the boundary conditions. For this purpose, a simplified four-axle bicycle model of the actual vehicle considering the vehicle body lateral and yaw dynamics while neglecting roll dynamics is used. The obstacle avoidance technique is mathematically modeled based on the proposed sigmoid function as the artificial potential field method. This potential function is assigned to each obstacle as a repulsive potential field. The inclusion of these potential fields results in a new APF which controls the steering angle of the autonomous vehicle to reach the goal point. A full nonlinear multi-wheeled combat vehicle model in TruckSim software is used for validation. This is done by importing the generated optimal path data from the introduced optimal path planning MATLAB algorithm and comparing lateral acceleration, yaw rate and curvature at different speeds (9 km/h, 28 km/h) for both simplified and TruckSim vehicle model. The simulation results show that the obtained optimal path for the autonomous multi-wheeled combat vehicle satisfies all vehicle dynamics constraints and successfully validated with TruckSim vehicle model.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".