Contact Representation in Robotic Mechanical Systems Employing Reduced Models
Bibliographic record
Abstract
Contact interactions play a major role in the dynamic analysis of robotic arms, where they can be represented as unilateral constraints. However, incorporating these contacts into the system dynamic model is a challenging task, given the numerous ways to account for them. This paper presents and compares two different approaches to contact modelling, highlighting how adopting a different perspective can avoid constraint redundancies and indeterminate problems. To this end, a co-simulation setup is employed as the primary framework to address the differences in the contact modelling approaches and the corresponding formulations. In a co-simulation setup, a system is divided into subsystems that exchange information at pre-determined communication points through the interface. Between the communication time points, the subsystems are integrated independently while they require updated interface variables from other subsystems. Hence, it is necessary to approximate these variables. In a model-based approximation, a reduced model of the subsystem emulates its dynamic behaviour at the interface. This paper addresses challenges in developing a representative reduced order model for a mechanical subsystem with contacts and proposes solutions to incorporate changes in contact states in the reduced model. It will be shown how basic assumptions in the contact dynamic incorporation can influence the simulation outcome. To demonstrate the proposed solution, a robotic arm model and its operations are used as a case study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".