A Practical Vision-Aided Multi-Robot Autonomous Navigation using Convolutional Neural Network
Bibliographic record
Abstract
In this paper, a low-cost practical approach for the collision avoidance of a multi-robot system by using a single camera. A convolutional neural network (CNN) is applied to obtain an estimation of the depth of the image at the output of a monocular camera, assisting the team of mobile robots to detect obstacles in an unknown environment, determining navigation strategies, overcoming the limitation of the onboard LiDAR sensor. An avoidance controller was designed over a modified artificial potential field (APF) method, leading robots to avoid obstacles to reach the goal point. This paper provides an alternative solution for range measuring and environment sensing, replacing common distance sensors such as LiDAR sensors and ultrasonic sensors. The camera captures more data about the environment while being relatively cheaper than most sensors. An open-source CNN machine learning model called MiDaS is applied to help estimate the depth of detected obstacles from the input image. Simulations and experiments with three 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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".