Hybrid‐triggered formation tracking control of mobile robots without velocity measurements
Bibliographic record
Abstract
Abstract This article focuses on solving the leader‐follower formation tracking problem of multiple mobile robots under a hybrid‐triggered mechanism without leader's velocity measurements. Two kinds of networks are considered: the detection network, which enables the relative detections by agents' onboard sensors, and the communication network, which is used to implement the transmissions of local information. The followers are divided into two groups based on their detection capacity of leader's information. By merely sensing the relative information from the leader, the first group of followers implement high gain observers to estimate leader's angular and linear velocities. In the rest followers, the transmitted information and relative detections from neighboring agents are used to estimate leader's velocities and position in a distributed way; after that, event‐triggered observer‐based controllers are proposed to drive the agents toward desirable formation. Periodic event‐triggered mechanisms (PETMs) are used to avoid continuous‐time checking of event‐triggering conditions; and the maximum allowable sampling periods (checking periods and transmission periods) are determined to guarantee the stability of the sampled‐data system. Since PETM is only applied in communication networks, the mechanism used in this work is a hybrid‐triggered one. Moreover, the inter‐sampling (‐checking, ‐transmission) times are allowed to be time‐varying and asynchronous. Finally, numerical examples are presented to illustrate the effectiveness and conservativeness of the proposed methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".