Inertia Compensation Using Flywheels in Parallel Robots for the Assisted Manipulation of Large Payloads
Bibliographic record
Abstract
This paper proposes the use of reaction wheels in parallel mechanisms for physical human-robot interaction during the co-manipulation of large payloads. The concept combines the advantages of a mechanically backdrivable robot - for hands-on-payload interaction - with the reactiveness of flywheels for the compensation of inertial loads, thereby leading to a smooth and low-inertia rendering. In the proposed approach, gravity compensation and dynamic compensation are partitioned and assigned to two subsets of actuators, namely the backdrivable joint actuators and the flywheel actuators, the latter being smaller and properly geared actuators to benefit from faster dynamics for interaction stability purposes. Simulation results of a human interaction with a planar robot to displace a payload show that the desired dynamic behaviour of the moving platform is correctly rendered, while indicating that the inertia compensation torques may vary more quickly than the gravity torques, which supports the proposed idea. Experiments are also conducted to validate the rendering of the desired virtual dynamics to the user.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 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".