Improved hybrid pneumatic-electric actuator for robot arms
Bibliographic record
Abstract
Robot arms require actuators that are powerful, precise and safe. In this paper we present the design and implementation of a novel rotary hybrid pneumatic-electric actuator (HPEA) for use in robot arms, particularly those intended for collaborative applications. It produces 3.5 times higher torque than prior HPEAs while maintaining the low mechanical impedance and inherent safety of the HPEA approach. Its low mechanical impedance results from its low friction and inertia. It has 450 times less inertia and 15 times less static friction than an industrial robot actuator with similar maximum continuous output torque. The design features four pneumatic cylinders connected in parallel with a small DC motor. The DC motor is directly connected to the output shaft. After the mechatronic design and system model are described, the control system design consisting of an outer position control loop and inner pressure control loop is presented. Experiments were performed with the actuator prototype rotating a link and payload with a rotational inertia equivalent to a linear actuator moving a 573 kg mass. Averaged over five tests, a root-mean-square error of 0.038° was achieved for upwards vertical moves. The steady-state error (SSE) was only 0.0045°, even when the arm was under maximum gravity load, primarily due to the adaptive friction compensator employed in the outer control loop. This SSE is almost ten times smaller than the best value reported for previous HPEAs.
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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".