Fast Trajectory Planning for AGV in the Presence of Moving Obstacles: A Combination of 3-dim A* Search and QCQP
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Bibliographic record
Abstract
This paper concerns about the automatic guided vehicle (AGV) trajectory planning scheme. Nominally it should be formulated as an optimal control problem (OCP) and solved via numerical methods. The concrete procedures to solve an OCP numerically include discretizing it into a mathematical programming (MP) problem and solving the MP via an appropriate solver. However, most of the predominant MP solvers only derive local optima because global optimization takes too long. As the predominant MP solvers only find local optima, the solution quality relies on the homotopy class of the initial guess, i.e. the starting point of an optimization process. A* search in the abstracted x-y-time state space is adopted to find a suitable initial guess, which directly plans a coarse trajectory rather than a path. With the initial guess, an MP in the form of a quadratically constrained quadratic program (QCQP) is solved easily. Simulation results show that the average CPU time spent on the first-A*-then-QCQP method is only l.4035 seconds in MATLAB. Source codes are provided at https://github.com/libai1943/AGV_Motion_Planning_with_Moving_Obstacles.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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