Design and development of soft robotic hand for vertical farming in spacecraft
Bibliographic record
Abstract
For colonization in deep space we need to explore the feasibility of a bioregenerative system in microgravity or artificial gravity environments. The process has various complexities form ranging to biological obstacles to engineering limitations of the spacecraft. Concentration of microbes in the confinements of a spacecraft can be fatal for the crew. In this paper, a solution to the elevated microbial levels by farming using robots is discussed. The soft robotic arm is made up of Asymmetric Flexible Pneumatic Actuator (AFPA). The AFPA under internal pressure will curve in the direction of the side having greater thickness as the expansion of the thinner side (outside radius) will be more than thicker side (inside radius) due to differential expansion and moment induced due to eccentricity. Simulation results demonstrate that bending based on AFPA can meet the designed requirement of application. The AFPA is used for five fingers of the robotic hand. The safe, soft touch and gentle motion of the bellow (AFPA) gives the feel of real human hand. The internal pressure of the AFPA is controlled using a solenoid valve which is interfaced using an Arduino microcontroller for hand like moves. The bending of the fingers and degree of freedom (DOF) of the joints of the hand is controlled using an IMU and flex sensor. Wireless connection of the hand and the control system is implemented using XBee pro 60mW with a range of 1 miles. The pneumatic soft robotic hand is made up of solenoid valve, Mini Compressor, AFPA bellow, and Servos. This soft robotic hand has many advantages such as good adaptability, simple structure, small size, high flexibility and less energy loss. As an extension Manual control of the robot using a virtual reality environment and well as some possible aspects of an automated farming systems can be considered as future additions.
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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) | 0.004 | 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".