Distributed Simultaneous Centroid Estimation and Formation Tracking Control Using Relative Position Measurement
Bibliographic record
Abstract
This paper presents a distributed framework for simultaneous rigid formation control and trajectory tracking in n dimensional space (n=2,3), motivated by coordinated multi-agent transport, cooperative surveillance, and fractionated spacecraft applications, which demand flexible and scalable alternatives to traditional single-agent approaches. In scenarios where only one agent knows its own global position, and each agent measures only the relative positions of its neighbors, the proposed framework integrates a two-layer decentralized estimator with distributed centroid tracking, orientation alignment, and formation maintenance controllers. The first estimator layer employs a consensus-based self-estimation law, ensuring exponential convergence to actual value. The second estimator layer extends this capability, allowing agents to estimate the positions of their peers, and hence cooperatively compute the formation centroid and orientation. Tracking of predefined formation centroid and orientation trajectories is achieved via three distributed control laws: one for formation maintenance, one for centroid trajectory tracking, and one for aligning the formation orientation, using unit complex numbers in 2D and unit quaternions in 3D. Lyapunov-based analysis establishes exponential convergence for all estimation and control components. Simulations demonstrate the formation’s ability to maintain geometric integrity, track trajectories, and achieve desired orientations, all within a distributed framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".