Exponential control barrier function and model predictive control for jerk-level reactive motion planning of quadrotors
Why this work is in the frame
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Bibliographic record
Abstract
Quadrotors require efficient reactive motion planning algorithms to ensure safe autonomous operations in dynamic environments. One common strategy for reactive motion planning is model predictive control (MPC); however, conventional MPC-based methods often fall short of guaranteeing collision avoidance when encountering dynamic obstacles. To address this limitation, we develop an enhanced framework that combines MPC with control barrier functions (CBFs) for improved safety and a Kalman Filter (KF) for predicting obstacle behavior, increasing responsiveness to dynamic obstacles. We utilize a high-order closed-loop model of the quadrotor along with exponential CBFs, enabling trajectory control at the jerk level, unlike existing MPC-CBF methods that rely on acceleration-level planning. Extensive hardware experiments across multiple scenarios demonstrate that this approach significantly enhances safety by increasing the minimum vehicle-obstacle distance and enabling successful navigation through complex situations, such as avoiding fast-swinging obstacles, where traditional MPC-only methods fail. Hardware-based sensitivity analysis further reveals the algorithm’s overall robustness to variations in parameter values, provides insight into parameter tuning, and highlights the critical role of accurate obstacle predictions in dynamic environments. Our findings indicate that the MPC-CBF-KF framework is a promising, robust, and computationally feasible solution for quadrotor motion planning in both dynamic and static environments. • Development of MPC-CBF-KF reactive motion planner for quadrotor navigation in dynamic environments, validated experimentally. • Integrating exponential CBF with a high-order quadrotor model to control the jerk of the trajectory rather than acceleration. • Hardware-based sensitivity analysis of key tuning parameters on the motion planner’s performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it