New Design of Minimized Torque and Actuators for Industrial Robot Arms
Bibliographic record
Abstract
Reducing the torque and number of actuators has received great attention because it minimizes both the initial and running costs. This paper introduces a new design for the robot end-effector that reduces the Degree of Freedom (DoF) from 6 to 3. A compressor is employed to generate a vacuum during a vacuum cup. Since atmospheric pressure equalizes itself and the air fills any missing gaps. This pressure moves and pushes against the air outside of the suction cup. This allows pulling and picking up plates of metal or glass in industrial applications. Also, all actuators (three actuators) are installed in the robot base. Then they are linked to a four-bar mechanism to transfer the power to each joint. The four-bar mechanism transfers the power from the actuators to move each joint. Four-bar linkage consists of three rigid moving links connected with the frame. The four-bar mechanism provides rotating and oscillating and relatively high flexibility(high redundant). Installing the actuators in the base makes the arm lighter than the conventional design thus reducing the required torque to operate each joint. The optimization of the robot to select the optimal material and cross-section area is conducted using the Finite Element Method. The torque derivation based on the Lagrange theory is presented. The reduced torque of each joint and total power has been evaluated and compared with the conventional ones. It is observed that the maximum percentage of reduction in the torque occurs at joint 2 (68.1 %) where the torque is reduced from 5.8 Nm to 3.5 Nm for 15 S trajectory time. Besides, it is found that the percentage of reduction depends on the trajectory time, the joint number, and the payload.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".