Formation Shaping Control for Multi-Agent Systems with Obstacle Avoidance and Dynamic Leader Selection
Bibliographic record
Abstract
This paper presents a novel approach to time-varying formation for the purpose of collision and obstacle avoidance using a displacement based formation algorithm. A team consisting of two-wheeled mobile robots as the agents is considered. A fast terminal sliding mode controller is used for the motion control of the agents. From arbitrary positions these agents move to a formation, and then navigate an unknown environment with multiple goal points. These agents use sensor data, such as measurements from ultrasonic sensors or lidar, to observe their environment and adjust the size of their formation in order to properly travel through the environment, as well as use an artificial potential field process for local collision and obstacle avoidance. This can be scaled up to any number of agents and could be applied to other types of agents. Simulations are presented which use both four and six agents, and show that the multi-agent system is capable of navigating an environment and that the leader agents will change to suit the needs of the formation as required.
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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 | 0.001 |
| 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 | 0.001 |
| 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".