Safe Urban Delivery by a Mobile Robot via CLF-CBF-QP Control Under Dynamic Obstacles
Bibliographic record
Abstract
To address dynamic obstacle avoidance in urban environments, this project presents a safety-critical control framework for autonomous delivery robots using a unicycle kinematic model. A Control Lyapunov Function (CLF) and Control Barrier Function (CBF) based Quadratic Programming (QP) controller ensures safe navigation and task completion. CLFs guarantee goal convergence despite environmental variations and moving obstacles, such as pedestrians [1, 2, 3]. CBFs enforce safety constraints in real-time, aligning with recent advancements in robotic control under dynamic conditions [4, 5]. Simulations in a Python/NumPy environment show a 97% safe task completion rate, delivery times within 10% of the optimal path baseline, and zero CBF violations, consistent with QP-based pathfinding literature [2, 3]. The framework prioritizes safety, enhancing public trust in autonomous robots, as supported by human-robot interaction studies [6, 7]. Ethical implications and public acceptance are discussed, emphasizing safe human-robot coexistence. All code, simulation data, and visual resources are publicly available to promote reproducibility and collaboration [8, 9].
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".