Design and Development of a Soft Pneumatic Gripper for Precise Grasping of Fragile Objects
Bibliographic record
Abstract
Soft robotics is an emerging subfield of robotics that studies the design and fabrication of automated systems composed of flexible materials. They present a potential solution to protect fragile objects from high stress induced by rigid grippers. This paper proposed a 3D printed soft pneumatic gripper adapted to precisely grasp delicate and fragile objects such as those encountered in the marine, electronic, and food industry. The gripper was based on the principle of a fluidic elastomer actuator and consisted of two soft TPU fingers and a rigid base with an Arduino-driven flexing sensor to measure the curvature of the fingers during grasping and a force sensor that enables precise measurement and adjustment of gripping force, ensuring the objects were held securely without damage. The design and fabrication were cost-efficient and engineered to not affect the continuous flexing of the soft fingers, addressing key challenges grasping with precision and efficiency. The relationship between the sensor outputs and pneumatic inputs were analyzed through graphs from conducted experimental tests.
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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 | 0.001 | 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".