Stream Function-Based Navigation for Dynamic Quadcopter Obstacle Avoidance
Bibliographic record
Abstract
This article presents a novel stream function-based navigational control system for obstacle avoidance, where obstacles are represented as two-dimensional (2D) rigid surfaces in inviscid incompressible flows. The approach leverages the vortex panel method (VPM) and incorporates safety margins to control the stream function and flow properties around virtual surfaces, enabling navigation in complex, partially observed environments using real-time sensing. To address VPM's limitations in avoiding rapidly accelerating obstacles at close proximity, the system integrates a model predictive controller (MPC) based on higher-order control barrier functions (HOCBF). This integration incorporates VPM trajectory generation, state estimation, and constraint handling into a receding-horizon optimization problem. Linear acceleration models and higher-order observers capture and predict obstacle dynamics, propagating the dynamic estimation into the MPC-HOCBF for rapid avoidance maneuvers. Evaluation is conducted using a PX4-powered Clover drone Gazebo simulator and real-time experiments involving a COEX Clover quadcopter equipped with a 360 degree LiDAR sensor.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 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".