Optimal Cooperative Maneuver Planning for Multiple Nonholonomic Robots in a Tiny Environment via Adaptive-Scaling Constrained Optimization
Why this work is in the frame
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Bibliographic record
Abstract
This letter is focused on the time-optimal Multi-Vehicle Trajectory Planning (MVTP) problem for multiple car-like robots when they travel in a tiny indoor scenario occupied by static obstacles. Herein, the complexity of the concerned MVTP task includes i) the non-convexity and narrowness of the environment, ii) the nonholonomy and nonlinearity of the vehicle kinematics, iii) the pursuit for a time-optimal solution, and iv) the absence of predefined homotopic routes for the vehicles. The aforementioned factors, when mixed together, are beyond the capability of the prevalent coupled or decoupled MVTP methods. This work proposes an adaptive-scaling constrained optimization (ASCO) approach, aiming to find the optimum of the nominally intractable MVTP problem in a decoupled way. Concretely, an iterative computation framework is built, wherein each intermediate subproblem contains only risky collision avoidance constraints within a certain range, thus being tractable in the scale. During the iteration, the constraint activation scale can change adaptively, thereby enabling to promote the convergence rate, to recover from an intermediate failure, and to get rid of a poor initial guess. ASCO is compared versus the state-of-the-art MVTP methods and is validated in real experiments conducted by a team of three car-like robots.
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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 | 0.000 | 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 | 0.000 |
| 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