Research on flexible control method of robot based on visual servoing
Bibliographic record
Abstract
Flexibility control and vision of robots are important acquisition and feedback links in robot control, and the study of multi-sensor data fusion is becoming more and more important as the complexity of robot tasks increases. This paper describes the robot kinematics and inverse kinematics process by studying the knowledge of D-H model theory and parameter definitions in the machine kinematics model, reveals the changing relationship between the robot joint control and end pose, and establishes a kinematics-based vision servo control model. On this basis, the coupling error compensation algorithm is used to combine the visual position control quantity as well as the force sensing position correction quantity to form the final visual and force sensing supple control strategy. Meanwhile, for the lack of adaptability of classical impedance force control on unknown constraint environments, a two-fuzzy adaptive sliding mode controller is designed according to the Lyapunov stability theorem to drive the robot end in order to achieve the actual position tracking expectation. The results of simulation experiments and motion contour tracking experiments show that the control algorithm proposed in this paper has better control accuracy and is more robust to noise and uncertainty, and the controller is also able to reduce the effect of torque saturation on the robot system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".