Trajectory tracking compensation for teleoperation with transmission delays
Bibliographic record
Abstract
SUMMARY The proper transmission of wave variables rather than power variables in teleoperation with time delays ensures system passivity – rendering the entire system stable, but the introduction of wave variables leads to distortion between the velocities/positions of the master and slave, and the performance deteriorates significantly with the increase of time delays. This paper presents a new compensating scheme implemented at the slave side to remove or cancel partially the distortion. The system passivity can still be maintained by tuning a properly designed low-pass filter. Compared with previous work, the main contribution of this work exists in two points: first, it is the actual velocity/trajectory of the slave rather than the reference velocity/trajectory of the slave that tracks the master one, so the quality of tracking is better and converges faster; second, the proposed compensator does not require any additional signal from the master side, minimizing the amount of data transmitted over the channel, which is very important from the practical point of view. The simulation and experiment results show that the velocity (or trajectory) tracking is significantly improved under the condition of stability, and the force presented to the user is close to the environment one, meaning a high degree of system transparency is achieved.
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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) | 0.000 | 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) | 0.001 | 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".