A Tensegrity-based Spherical Mobile Robot with Multiple Rolling Gait Patterns
Bibliographic record
Abstract
In recent years, tensegrity structures are being increasingly used in mobile robots because of their outstanding properties, such as impact resistance, large load-to-weight ratios, and self-balancing. However, most rolling tensegrity robots use center-of-mass offsets to move, making the robots inefficient and less maneuverable. In this paper, a propulsion induced spherical movable tensegrity robot which can realize multiple rolling gait patterns is proposed. Firstly, the novel rolling tensegrity robot is designed by integrating a 6-rod spherical tensegrity structure and 12 propellers, and its multi-gait rolling mechanism is presented. Based on this design, not only are the three traditional rolling gaits along the sides of the ground contact triangle realized, but also three new rolling gaits around the vertices of the triangle. Secondly, the driving forces required for the six rolling gaits are analyzed and then the step length and path space of the two types of gaits are analyzed. Finally, a prototype of the proposed robot is developed, and experiments are carried out. The results show that the proposed robot exhibits superior gait versatility and improves rolling efficiency and maneuverability compared to traditional tensegrity robots.
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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) | 0.002 | 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".