Five Bar Planar Manipulator Simulation and Analysis by Bond Graph
Bibliographic record
Abstract
This work focuses on the bond graph modelling method and its application on multi-body system, especially on the five-bar parallel robot. Five-bar parallel robot is comprised of four arms, two revolute actuators and five revolute joints. This paper adopts five-bar parallel robot in symmetric configuration as simulation object. As it will be used as a pickup and placing machine, its workspace is fixed on Cartesian coordinate. The relationship between the two rotating angles and end effector’s desire position is built by inverse kinematics. Bond graph is used to describe moment, torque, velocity, angle relationships. In this project, the dynamic performances between arms, motors at robot basement and end effector will be researched. In this paper, an investigation about how to use bond graph to model DC (direct current) servo motor and an integrated motion control system is carried out. During a typical end effector point-point displacement, the torque change between arms is plotted. Finally, 3-D animation experiment is conducted. Experiment results show that bond graph can simulate robot dynamics performance without having to make a large number of equations. It is able to simulate and solve five-bar kinematics problem in the process.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".