A study of the applications of a spring-assisted modular and reconfigurable robot for safe robot operation
Bibliographic record
Abstract
A novel spring-assisted modular and reconfigurable robot (SA-MRR) has been recently developed at our laboratory to reinforce its performance, and to enable safe and dexterous operations in human environments. A power spring is inserted between the brake rotor and the motor shaft through a decoupling bearing. With the spring engaged, the working range of the joint is mechanically limited for safe operations, and such a limited working range can be established at any joint position. The safety aspect of the SA-MRR is investigated by operating the robot in a limited workspace created by activating the spring. The trajectory tracking capability of the SA-MRR is explored by comparing trajectories followed by a conventional MRR and SA-MRR in a restricted workspace, while lifting a heavy payload. Trajectory tracking is performed with various payloads to demonstrate the SA-MRR’s superior payload handling capacity performance due to addition of the spring-generated moment. These algorithms have been implemented on a 3-DOF SA-MRR and numerical simulations have been carried out to investigate the improved tracking accuracy and safety features due to addition of the spring-brake system.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".