LEADER-FOLLOWER FORMATION CONTROL OF MULTI-ROBOTS BASED ON BEARING-ONLY OBSERVATIONS
Bibliographic record
Abstract
This study proposes the bearing-only leader-follower formation control method and examines the nonlinear observability properties of the leader robot system.A study of the nonlinear observability properties between the leader robot and landmarks shows that the system is completely observable when the leader robot can observe four different landmarks.A subsequent study of the leader-follower formation control shows that when the leader robot system satisfies the observability condition of the nonlinear system, the system output can convey sufficient information to allow the observer to provide a correct estimate of the state.Consequently, multi-robots can quickly form and maintain a formation based on the following sufficient bearing-only information, which is that follower robots observe the leader robot.In leader-follower formation, the unscented Kalman filter is employed to estimate the states of the leaderfollower robot system.Based on this system, the input-output feedback control law is executed to control the real-time movement of the followers, which allows the leader-follower formation to be properly maintained.Finally, simulation results are presented to demonstrate that the proposed approach can efficiently control the formation of multi-robots as desired.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".