Integrating Industrial Robots Through a Contact Method
Bibliographic record
Abstract
The evolving industrial paradigm demands high quality procedures and automation to adapt to dynamic market conditions. As a consequence, novel robotic systems are integrated within the current digital industrial environments ensuring real-time adaptation to production changes and safe operation with humans and machines. Collaborative robots (cobots) and Autonomous Mobile Robots (AMRs) are designed to follow these principles enhancing the current digital industrial revolution. However, while AMRs are subjected to navigation errors, cobots incur in positioning errors due to the stepper motors operation, resulting in a combined interaction error that do not allow a direct accurate operation between these robots. The addressing of this challenge requires the definition of a robust integration framework between these robots that enables the compensation for the interaction errors occurring during their joint operation. To achieve this challenge, in this paper, we propose a novel Fictitious Points Compensation Methodology (FPCM) based on contacts on the AMR structure and an appropriate error characterization that compensates the interaction errors, proving that these errors are systematic. Practically, we make use of fictitious points that define the final position of the cobot end-effector to minimize the final interaction error. The validity of the proposal is verified through two practical cases showing angular errors up to 0.04°, and placement errors up to 0.13 mm, which validates both the proposal and the error characterization. Thus, our method efficiently addresses the challenge of integrating heterogeneous robotic systems within the current industrial paradigm.
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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 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".