Teleoperation of a Mobile Robot using Predictive Control Approach
Bibliographic record
Abstract
Nowadays, teleoperation has a wide variety of applications. For example in a bilateral teleoperation system, a human can control a robot or a system which is far away from each other. One of the major problems faced in a bilateral teleoperation system is the time delay due to the transmission of data between the master and the slave sides. If the controller is not properly designed, the delay will degrade the closed - loop performance and even worse, it will destabilize the bilaterally controlled teleoperator. In order to minimize the effect of the time delay we chose a predictive control approach to improve the system performance while at the same time it can retain the stability of the whole teleoperation system. Here the human operator dynamics is not taken into account and hence less prior information is required for the design when compared with earlier proposed control schemes. Another main contribution of this paper is that various comparison results with conventional schemes are extensively discussed. Finally, the simulation results further show the effectiveness of the proposed approach.
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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".