Motion Planning and Control of Quadrotor Based on MPC in Frenet Frame
Bibliographic record
Abstract
The autonomous flight of Unmanned Aerial Vehicles (UAVs) hinges on the interplay between trajectory planning and flight control. This study centers on a specific case: the autonomous flight of a quadcopter at low altitudes. To enhance this autonomous flight process, we address the estimation and prediction of trajectories for dynamic obstacles that may intersect the UAV's low-altitude flight path. This is achieved through the utilization of Extended Kalman Filtering (EKF) and Joint Probabilistic Data Association (JPDA) techniques within the Frenet coordinate system. Subsequently, leveraging both global planning and real-time environmental data, the local motion planner constructs an optimal trajectory for the UAV on a local scale. This ensures that the UAV navigates its immediate surroundings efficiently. To execute this trajectory, the quadrotor UAV undertakes low-altitude flight while adhering to the locally optimized path. This is accomplished through the application of Model Predictive Control (MPC), which enables the UAV to autonomously avoid collisions in real time. Finally, the validity and viability of the proposed approach are substantiated.
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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 | 0.000 | 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 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".