Comparative study of dynamic programming and Pontryagin's minimum principle for autonomous multi-wheeled combat vehicle path planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a comparative study of two path planning algorithms using optimal control theory for the autonomous multi-wheeled combat vehicle. The developed optimal path planning algorithms use Pontryagin's minimum principle (PMP) and dynamic programming (DP) approaches. PMP and DP are two major branches of the optimal control theory. A simplified two degrees of freedom (DOF) vehicle model is used to derive the differential equations of the vehicle. The cost function associated with the path generation is to be minimised with the vehicle dynamics equations. A comparative study and performance analysis of generated optimal paths using the proposed algorithms was carried out for various scenarios. The simulation results demonstrate that the generated optimal solution using PMP is very close to the DP solution, which is the guaranteed global optimum. In addition, the initial and final condition parameters and the vehicle dynamics are satisfied. However, the PMP computation time is significantly less than the DP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it