Distributed MPC based Collision Avoidance Approach for Consensus of Multiple Quadcopters
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the problem of distributed model predictive control (MPC) based collision avoidance among a team of multiple quadcopters attempting to reach consensus is investigated. A team of quadcopters trying to reach consensus or formation may collide with each other in the space if without a collision avoidance mechanism. A quadcopter receives neighbours positions and can determine a next desired position using a consensus protocol. Distributed MPC is used to develop a set of predicted relative distances along the prediction horizon. From these predicted relative distances, violations of a predetermined safety distance generates output constraints on the MPC optimization function. In literature, most strategies consider evasive action in the z-direction or only consider movement on the x-y plane. Different from past works, the proposed strategy performs collision avoidance by selecting a predetermined evasive direction in the x, y, or z-directions. This work also offers consensus in the x, y, z, ψ-directions, limits on control actions using logarithmic barrier functions and fourth-order quadcopter dynamics. The proposed algorithm was simulated for a team of quadcopters. Simulation results show that constraints on the controlled variables allows agents to converge to a consensus formation without collision.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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