Coordinated Motion and Force Control of Multi-Rover Robotics System with Mecanum Wheels
Bibliographic record
Abstract
This paper presents a novel optimal control algorithm for coordinated force and motion control of multi rover robotics system with mecanum wheels while manipulating a common payload. Such a system with kinematical rolling conditions lead to non-holonomic constraints. The proposed control algorithm focuses on the minimization of joint torques, the rover-mecanum wheel moments as well as the contact force / moments made with the payload. A quadratic cost function in terms of the joint torques, the wheel moments and the contact forces and moments are minimized to overcome the so called joint torque saturation problem commonly seen while manipulating a common payload and also to provide an optimum solution for such an underdetermined system with non-holonomic constraints Furthermore, the proposed control algorithm provides an on-line trajectory generation capability while manipulating a common payload for both the rovers and the arms simultaneously. The computer simulation results show that the control algorithm works efficiently and the minimum joint torques, and the contact forces and moments can be obtained while the end-effectors are manipulating and tracking a desired payload trajectory.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".