A Receding Horizon Trajectory Tracking Strategy for Input-Constrained Differential-Drive Robots via Feedback Linearization
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Bibliographic record
Abstract
This brief proposes a novel solution to the trajectory tracking control problem for input-constrained differential-drive robots. In particular, we develop a robust set-based receding horizon tracking scheme capable of dealing with state-dependent input constraints arising when the vehicle’s dynamics are approached by a standard feedback linearization (FL) technique. First, offline, we characterize the worst case input constraint set and compute an admissible, although not optimal, controller. Then, online, we leverage the knowledge of the robot’s orientation to enlarge the constraint set in a receding horizon fashion and, consequently, improve the tracking performance. Recursive feasibility and constraints’ fulfillment are formally proven. The approach’s effectiveness is experimentally validated on a Khepera IV differential-drive robot by comparing the control performance with several competitor schemes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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