Command Strategies for Tele-Operation of Mobile-Manipulator Systems via a Haptic Input Device
Bibliographic record
Abstract
Tele-operation of mobile-manipulator systems (MMS) is typically done by modeling both the manipulator and mobile base as a single system combined with the use of various redundancy resolution techniques to achieve coordinated motion. In this work, the Omnibot MMS is controlled by a 3-DOF (degrees-of-freedom) haptic joystick. The base is a holonomic platform having 3-DOF supporting a 3-DOF manipulator capable of position control within its workspace. By controlling the base and manipulator individually, redundancy resolution techniques are not required because the master has equal DOF to the device it is controlling at any given time. Two command strategies are proposed herein for automatic switching of control between the manipulator and base. The first command strategy is a virtual wall method that uses haptic forces to make the operator feel like they are pushing against a wall at the edge of the manipulator’s workspace, resulting in the base moving. The second command strategy is a two state approach in which each state controls a different device and switching between the two is done by breaking through the workspace limits. With the help of haptic forces, the operator has the sensation of breaking through a compliant surface when switching states. The two proposed strategies were implemented and tested on the Omnibot MMS and both subjective and objective data was used to evaluate and compare the two methods.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".