Formation Shaping Control for Multi-Agent Systems with Obstacle Avoidance and Dynamic Leader Selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it