Distributed Control for Time-Varying Formation Acquisition and Tracking With Orientation Alignment in Multivehicle Systems
Bibliographic record
Abstract
In multiagent coordination tasks, motion trajectories are required to satisfy a range of constraints that present significant implementation challenges due to the limited onboard sensing and communication capacities. This article introduces distributed control laws that integrate nonholonomic motion constraints into bearing-based designs to enable time-varying formation tracking with minimal onboard resources. Unlike state-of-the-art formation control solutions, this approach maintains formation shape through relative bearing feedback and orientation alignment rather than tracking global target locations or regulating interagent relative positions and velocities. This distributed controller design has been validated in two deployment scenarios: 1) leaderless nonhierarchical formations and 2) leader-follower hierarchical formations. In hierarchical formations, follower agents employ a speed estimator within the orientation alignment framework to reach velocity consensus with the leader agent. The proposed controllers guarantee accurate tracking of time-varying reference trajectories, preserve the desired formation structures, and achieve velocity consensus for both nonhierarchical and hierarchical formations, as established by analysis and validated through simulations and experiments.
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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".