Dynamic simulation of serial robots under force control
Bibliographic record
Abstract
The advantages of using force control in industrial robots are well known. Study of such systems in virtual environments in the form of simulation is of great help as most of the force controlled task works in close contact with the environment. In this paper, we show how to simulate different force control algorithms of a typical serial robot used in industries before deciding to choose a suitable one for real implementation. Hence, a proper dynamic model of the robot is essential which should be able to emulate the real robot, particularly if the robot moves at relatively higher speeds. This is done here using the concept of the decoupled natural orthogonal complement (DeNOC) matrices which is known to provide a recursive forward dynamic algorithm that is not only efficient but also numerically stable. Such simulation of robots under force control will allow users to tune the control gains without stopping the real robot on the production floor. Besides, such simulation can be used as an education tool as well to help beginners to explore various types of control algorithms and their performances. In addition, the framework for simulation proposed in this paper can work as a good test bench to test the performances of either a new control law or a different dynamic algorithm. As an illustration, the DeNOC based dynamics was substituted with MATLAB's SimMechanics which can also perform dynamic simulation. The comparison of the results validated the concept and correctness of the numerical simulations.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".