A New Modular, Autonomously Reconfigurable Manipulator Platform
Bibliographic record
Abstract
This paper discusses the design and development of a new Modular, Autonomously Reconfigurable Serial manipulator platform for advanced manufacturing, termed as the MARS manipulator. The platform consists of i) an 18-Degree-of-Freedom (DOF) serial-link manipulator capable of locking any of its joints at any position in their continuous range, such that it can emulate fewer-DOF serial manipulators with different kinematic and dynamic parameters, and ii) an integrated simulation and design environment that provides control over the manipulator hardware as well as a toolset for the design, implementation and optimization of a desired manipulator configuration for a given task. The effectiveness of the MARS manipulator to adapt its configuration to various tasks is examined by assuming two well-known configurations, SCARA and articulated, and by performing a specific task with each of them. The variation in effectiveness of the two configurations in terms of the end-effector trajectory, end-effector accuracy and power consumption is discussed. Further, these configurations are optimized with respect to their performance accuracy, and compared to their pre-optimized versions. Finally, the accuracy model of the simulation is compared against the physical hardware system, running the same task.
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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) | 0.003 | 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".