Collision-Free Formation Control and Tracking for Multi-Agent Systems under Motion Constraints
Bibliographic record
Abstract
The cohesive motion of a group of vehicles remains stable when all agents achieve velocity consensus and maintain a constant desired speed. However, when the reference velocity changes over time, the formation tracking controllers may struggle to ensure collision-free motion during transitional phases. In this paper, the simultaneous formation control and tracking task is formulated as a constrained optimization problem where the control input actions are restricted within a defined search space. The control objective is to ensure a desired formation geometry while tracking a time-varying reference trajectory by satisfying inter-agent separation conditions for collision avoidance and adhering to input saturation limits that define the allowable control space. Due to the sensor limitations of the deployed agents, the inter-agent distances are estimated using only relative bearing and velocity measurements. The stability and tracking convergence of the proposed distributed control and observer designs are analyzed and validated through simulations in a multivehicle deployment scenario. The results show that the control inputs stay within the feasible limits and effectively prevent inter-agent collisions while achieving the control objectives.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".