Design and Analysis of a Hybrid Mobile Robot Mechanism With Compounded Locomotion and Manipulation Capability
Bibliographic record
Abstract
This paper presents a novel design paradigm as well as the related detailed mechanical design embodiment of a mechanically hybrid mobile robot. The robot is composed of a combination of parallel and serially connected links resulting in a hybrid mechanism that consists of a mobile robot platform for locomotion and a manipulator arm for manipulation. Unlike most other mobile robot designs that have a separate manipulator arm module attached on top of the mobile platform, this design has the ability to simultaneously and interchangeably provide locomotion and manipulation capability. This robot enhanced functionality is complemented by an interchangeable track tension and suspension mechanism that is embedded in some of the mobile robot links to form the locomotion subsystem of the robot. The mechanical design was analyzed with a virtual prototype that was developed with MSC ADAMS software. The simulation was used to study the robot’s enhanced mobility characteristics through animations of different possible tasks that require various locomotion and manipulation capabilities. The design was optimized by defining suitable and optimal operating parameters including weight optimization and proper component selection. Moreover, the simulation enabled us to define motor torque requirements and maximize end-effector payload capacity for different robot configurations. Visualization of the mobile robot on different types of virtual terrains such as flat roads, obstacles, stairs, ditches, and ramps has helped in determining the mobile robot’s performance, and final generation of specifications for manufacturing a full scale prototype.
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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 | 0.000 |
| 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".