Distributed MPC based Collision Avoidance Approach for Consensus of Multiple Quadcopters
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".