Bilateral Teleoperation of a Multi-Robot Formation with Time-Varying Delays using Adaptive Impedance Control
Bibliographic record
Abstract
This paper proposes a decentralized adaptive impedance control approach to the bilateral control of a team of mobile robots under time-varying delays. A master-slave framework is developed and a decoupled approach is taken to ensure stability and allow for different formation controllers to be implemented. A novel decentralized method of estimating the center of formation is formulated. An adaptive impedance controller is proposed where the impedance parameters are functions of the error between the estimated center of formation and desired center of formation. In comparison to traditional tank-based passivity control for the teleoperation channels, this approach provides more accurate stiffness following and fewer open parameters that must be tuned. The purpose of the force feedback is to reflect the information of the environment to the operator to provide a transparent representation of the environment and the formation performance. This feedback encourages the operator to command the team in a motion that maintains formation and maneuvers around obstacles. Simulations and experiments with a Phantom Omni haptic device and three TurtleBot3 mobile robots are conducted to validate the proposed framework in the presence of time-varying delays.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".