Research on high-precision localization method for transport robots in industrial environments based on Improved AMCL and QR code assistance
Bibliographic record
Abstract
The application of handling robots in industrial environments has always been a research hotspot. This paper proposes a positioning scheme for handling robots based on improved adaptive Monte Carlo (AMCL) fusion of multiple sensors and QR code assistance, which can achieve high-precision positioning under low-cost conditions in industrial environments, in response to the positioning accuracy and cost issues of handling robots. Firstly, this article uses the Cartographer algorithm to fuse data from multiple sensors and improve map accuracy. Secondly, this article proposes an improved AMCL algorithm that integrates multiple sensors for localization, enhancing global localization accuracy. Then, in order to further improve the local positioning accuracy, the two-dimensional code assisted positioning system is activated to correct errors when approaching the work point, thereby achieving high-precision positioning near the work point. Meanwhile, utilizing the YOLO Fastest algorithm based on DNN inference framework to improve the efficiency of camera recognition of QR codes. Finally, the transport robot was tested in an industrial environment. The results show that the positioning error of the scheme in the x direction of the workstation point is ± 0.068 m, the positioning error in the y direction is ± 0.069 m, and the heading angle error is ± 0.107 rad. Experimental results have shown that this study helps promote the use of low-cost control methods to achieve high-precision positioning of handling robots in industrial environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".