Leader-follower formation control of nonholonomic mobile robots subject to robots failure
Bibliographic record
Abstract
Over the years, control of autonomous vehicles in a defined formation has been the subject of much research. Albeit leader-follower approach being one of the most used in formation control, it suffers a major practical drawback of leader failure while cruising in formation. In this work, we aim to solve this problem by proposing a novel assignment algorithm that assigns a new leader from the follower robots to ensure robots complete their given task when their leader fails. This algorithm also assign role to new robots joining the group, as well as the failed robot when rescued back to the team. We drive robots towards their desired trajectories to achieve formation using a Lyapunov-based time-varying state tracking controller from the literature. Due to role switching amongst member robots, we propose a new variant of the limit-cycle obstacle avoidance algorithm to ensure smooth and collision free transition. Simulations and experiments are performed using the robot operating system framework due to its flexibility to verify the effectiveness and reliability of the proposed algorithms.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".