Vision-based Navigation for the Affine Formation Control of a Multi-Robot Team
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
In this paper, a practical approach is proposed for the collision avoidance of a multi-robot system by using a single depth camera for one leader agent. An Intel RealSense D435i camera is applied for the specified leader to navigate and avoid obstacles in an unknown environment, assisting the team of mobile robots in detecting obstacles, determining navigation strategies, and overcoming the limitation of the onboard 2D LiDAR sensor. The proposed approach doesn’t require the mapping of the navigation environment in advance, which is very useful in many applications for multi-robot teams. This paper provides an alternative effective solution for range measuring and environment sensing, replacing common distance sensors such as 2D LiDAR sensors and ultrasonic sensors. The depth camera captures more data about the environment while being relatively accurate in estimating distances than monocular cameras. An affine formation control method is applied to keep the multi-agent system in and change to desired rigid shapes. Simulations and experiments with four TurtleBot3 mobile robots were conducted to validate the proposed algorithms. Experimental studies have been carried out to test the effectiveness of the proposed approach in the paper.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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