Autonomous Robot for Mining Exploration: A Structomatical and Kinematical Model for Uneven Ground
Bibliographic record
Abstract
This paper deals with the analysis of the mechanical system of a self-propelled vehicle on the tires able to move on an uneven ground whilst his platform stays horizontally. It is question to simulate the movement of a desmodromic robot which moves in an environment represented by a 3D surface. The robot has a mechano-hydraulic system which is able to modify the geometry of chassis in the aim of maintaining the platform always at horizontal while in movement, no matter the soil configuration (of course between some limits).The horizontalisation mechanism with the rolling train hydraulically driven presents some difficulties because of the non holonomic constraints of the wheels ([1, 2]). In order to make the application of the multipoles theory in the structomatical model must be introduce some simplifications in the contact joint. Thus, the non holonomic joints are presented like gamma active joints (with the condition of controlled rolling/skidding).This is an extension of the general principle of mechanism formation ([3]) according who any mechanical structure can be broken in genes upon an unique formula (the genetic code of the mechanism).Because of the complexity of the calculus the study of mechanism was divided in a few chapters: geometrics, structomatics, kinematics, kinetostatics and dynamics etc. The uttermost important and difficult part is the kinematical model because of the non-linearity of the equations. This article presents the first two items; the others will be the matter of future papers.
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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 | 0.001 | 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) | 0.003 | 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".