Real-time Optimal Control of Multi-wheeled Combat Vehicles - using Artificial Neural Network and Potential Fields
Bibliographic record
Abstract
This paper presents a real-time path planning algorithm for autonomous multi-wheeled combat vehicles using Artificial Neural Network (ANN), Artificial Potential Fields (APFs) and optimal control theory. Real-time navigation of autonomous vehicles is a very complex problem and it is crucial for many military operations. This paper proposes an optimal control and ANN approach for a dynamic model of the multi-wheeled combat vehicle to generate the possible optimal paths that cover every part of the workspace. Consequently, the obtained paths are used to train the proposed ANN model. The trained ANN has the capability to control the movement of combat vehicle in real time from any starting point to the desired goal position within the area of interest. The vehicle path is autonomously generated from the previous vehicle location parameter in terms of lateral velocity, heading angle and yaw rate of the vehicle. APF is proposed for preventing the vehicle from colliding with obstacles in border destination. The effectiveness and efficiency of the proposed approach are demonstrated in the simulation results, which show that the proposed ANN model is capable of navigating the multi-wheeled combat vehicle in real time.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".