Virtual Prototyping for Conceptual Design of a Tracked Mobile Robot
Bibliographic record
Abstract
Mobile robots equipped with manipulator arms are often required to operate on rough and uneven terrains. During the design stage, the designer needs to verify the robots performance on various test terrains. Physical prototyping although is more desirable, may prove to be expensive and time consuming. Virtual prototyping can be used especially in the conceptual design stage in order to significantly reduce the amount of physical testing that is required. This paper discusses the application of virtual prototyping using ADAMS software for tracked mobile robots. The virtual prototype was created for an existing robot and was validated with tests done on the physical robot. Different types of virtual terrains were created and dynamic simulations of the robot were performed on these terrains. The simulation results helped in many ways including visualization of the robot motion on different terrains and in determining the limiting dimensions of obstacles, stairs, ditches, etc., that the robot can safely negotiate. The described process can be applied to new robot designs, while still in their conceptual design stage
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".