Nonlinear Model Predictive Control for Omnidirectional Robot Motion Planning and Tracking With Avoidance of Moving Obstacles
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a nonlinear model predictive control algorithm for online motion planning and tracking of an omnidirectional autonomous robot. The formalism is based on a Hamiltonian minimization to optimize a control path as evaluated by a cost function. This minimization is constrained by a nonlinear plant model, which confines the solution space to those paths which are physically feasible. The cost function penalizes tracking error, control amplitude, and the presence in a potential field cast by moving obstacles and Boards. An experiment is presented demonstrating the successful navigation of a field of stationary obstacles. Simulations are presented demonstrating that the algorithm enables the robot to react dynamically to 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 | 0.000 | 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