Enhancement of Force Exertion Capability of a Mobile Manipulator by Kinematic Reconfiguration
Bibliographic record
Abstract
With the increasing applications of wheeled mobile manipulators (WMMs), new challenges have arisen in terms of executing high-force tasks while maintaining precise trajectory tracking. A WMM, which consists of a manipulator mounted on a mobile base, is often a kinematically redundant robot. The existing WMM configuration optimization methods for redundant WMMs are conducted in the null-space of the entire system. Such methods do not consider the differences between the mobile base and the manipulator, such as their different kinematics, dynamics, or operating conditions. This may inevitably reduce the force exertion capability and degrade the tracking precision of the WMM. To enhance the force exertion capability of a WMM, this letter maximizes the directional manipulability (DM) of the manipulator, with consideration of the joint torque differences, first in Cartesian space and then in the null-space of the robotic system. To maintain precise end-effector trajectory tracking, this letter proposes a novel coordination method between the mobile base and the manipulator via a weighting matrix. The advantages and effectiveness of the proposed approach are demonstrated through experiments.
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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 | 0.000 |
| 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".