Bibliographic record
Abstract
Spherical rolling robots (SRR) have been a promising avenue for the exploration of unstructured environments with variable topologies. The advantages include the ability to move fast, robustness to collision, and a lower number of actuators. However, to finally be used in real missions and applications, they need to have a high maneuverability and have sufficient inner space to house a proper payload for the intended application, such as cave and tunnel exploration, without compromising on the performances. With barycentric spherical robot, adding mass with a payload may become challenging, as the location of the center-of-mass is critical for the locomotion. In this article, we propose a novel barycentric spherical robot with two degrees-of-freedom (DoF) named Autonomous Robotic Intelligent Explorer Spheres (ARIES). The motion of this SRR is generated by a cylindrical actuated joint acting like a 2-DoF pendulum. This design allows us to have a nearly empty upper hemisphere inside the spherical shell, which is dedicated to payloads adapted to the application. The full kinematics and dynamics are presented, and simulation results are included. The control scheme implemented is detailed. We conducted an experimental evaluation of the ARIES with different trajectories, as well as discussed practical considerations and future improvements.
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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 | 0.001 |
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".