HEAVY OBJECT MANIPULATION BY A HYBRID SERIAL-PARALLEL MOBILE ROBOT
Bibliographic record
Abstract
In this study, dynamics and control of a compounded serial-parallel wheeled mobile robot is elaborated.The proposed system is made of a differentially-driven wheeled platform, a planar parallel manipulator, which is called here as star-triangle (ST) mechanism, and a serial Puma-type manipulator arm attached to the moving star-plane of the parallel manipulator.The suggested structure adopts the advantages of both serial and parallel robots, which will guarantee stable manoeuvres for heavy object manipulation by a mobile robotic system.To develop a comprehensive kinematics model of the robot; it is divided into three modules: i.e., a mobile platform, a parallel ST mechanism, and a serial manipulator.Next, a closed-form dynamics model is derived for the whole hybrid system based on a combined Newton-Euler and Lagrange formulation.The proposed method presents the mutual dynamic interaction wrenches between the integrated platform and the serial manipulator which can be exploited for the tip-over stability analysis of the mobile robotic system.Finally, the multiple impedance control (MIC) law is adopted to control the overall system in either free of contact missions or interactive tasks.The MIC law as a model-based algorithm enforces a desired impedance law on the platform, ST parallel mechanism, and the serial manipulator itself.Obtained results reveal a good tracking performance of the system, i.e., a smooth motion of the manipulator and the moving base, even in the presence of impacts due to contact with an obstacle.
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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) | 0.000 | 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 | 0.001 |
| 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".