Ease of dynamic modelling of wheeled mobile robots (WMRs) using Kane's approach
Bibliographic record
Abstract
This article illustrates the ease of modelling the dynamics of wheeled mobile robots (WMRs) using Kane's approach for nonholonomic systems. For a control engineer, Kane's method offers several unique advantages over Newton-Euler and Lagrangian approaches used in available literature. Kane's method provides a physical insight into the nature of nonholonomic systems by incorporating the motion constraints as part of the derivation. The presented approach focuses on the degrees of freedom and not on the configuration, and this eliminates redundancy. Explicit expressions to compute the dynamic wheel loads needed by tyre friction models are derived. This paper describes a procedure developed to deduce the dynamics of a differentially driven WMR with suspended loads and operating on various terrains. Since Kane's approach provides a systematic modelling scheme, the method proposed in this paper can be easily generalized to model WMRs with various wheel types and configurations and for various loading conditions. The dynamic model is mathematically simple and is suited for real time control applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".