An agile assistant robot integrating operation and rolling locomotion
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose an agile assistant robot to be used as a mobile partner with two rotational motions and one translational motion. This robot possesses the rolling function and three operating abilities to assistant human beings in the industrial environment. Design/methodology/approach The main body of the robot is a typical 4-RSR (where R denotes a revolute joint and S denotes a spherical joint) parallel mechanism. The mechanism can reach any position on the ground by two rolling modes (the equivalent Watt linkage rolling mode and the equivalent 6R linkage rolling mode), and the robot can work as a spotlight or a worktable in operating modes at the target location. The mobility, rolling modes, operating modes and kinematics are analyzed. Findings Based on the results of kinematics of this assistant robot, the upper platform of the 4-RSR rolling mechanism has two rotational motions and one translational motion which can be used in the industry. The proposed concept is verified by experiments on a physical prototype. Practical implications This paper also discusses the application to industrial cases where cooperation between workers and robots is required. Originality/value The work presented in this paper is a novel exploration to apply parallel mechanisms to the field of assistant rolling robots. It is also a new attempt to use the rolling mechanism in the field of mobile operating robots for industry tasks.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".