Finite-Time Control Barrier Functions for Safety-Critical Control of Discrete Systems with Application to Robot Navigation
Bibliographic record
Abstract
This paper proposes a unified finite-time control framework for safety-critical control of nonlinear discrete-time systems. First, a novel concept of finite-time discrete control barrier function (FT-DCBF) is introduced to ensure finite-time safety recovery, along with the definition of a strong safety set whose forward invariance is rigorously proven. Subsequently, the discrete control Lyapunov function (DCLF) is integrated with the proposed FT-DCBF within a quadratic programming (QP) framework to construct a unified finite-time safety controller. To demonstrate the effectiveness of the proposed method, it is applied to obstacle avoidance navigation for wheeled mobile robots (WMRs). Under disturbance conditions, the proposed control strategy significantly enhances the navigation safety and robustness of the WMRs. Finally, simulation results validate the efficacy of the proposed control framework.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".