A new bilaterally teleoperated robotic vehicle platform with passivity control
Bibliographic record
Abstract
In this paper, a new bilateral teleoperated vehicle testing platform is proposed. In the platform, we apply a new Power-based Time Domain Passivity Control (PTDPC) method in an effort to improve the transparency. The platform is realized where steering angle commands were transmitted from a steering wheel interface to a remote vehicle, and road surface forces are sent back from the vehicle to the steering wheel interface. The communication channel between the steering wheel (master side) and the remote vehicle (slave side) introduces time varying delays in the transmitted signals. To ensure the stability, PTDPCs are applied to each side of the communication channel, which further improve the tracking performance as well and reduce the overall effort required of the human operator. The alternative advantage is the simplicity: the dynamic models of both master and slave side systems are not required to be known. The algorithms, hardware and software realizations are described thoroughly and experimental results are demonstrated to show the effectiveness of the proposed approach as well as the functionality of the platform.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".