Robust Cascade Vision/Force Control of Industrial Robots Utilizing Continuous Integral Sliding-Mode Control Method
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a robust cascade vision/force approach to control industrial robots interacting with unknown workpieces considering model uncertainties. This cascade structure, consisting of an inner vision loop and an outer force loop, avoids the conflict between the force and vision control in the traditional hybrid methods without decoupling force and vision systems. To apply an advanced image-based visual servoing (IBVS) compensator, some newly modified image features are used that render an invertible image interaction matrix. A practical task-based method is proposed to extract the features corresponding to the desired path in a three-dimensional space. An adaptive robust Kalman filter is adopted for filtering the noise in the force feedback signal. A robust continuous integral sliding-mode control (CISMC) method is developed for both IBVS and force compensators. CISMC exploits the advantages of the modified supertwisting algorithm to reduce the chattering. The stability of the proposed cascade controller is proved. Additionally, a contact detector algorithm is developed to manage the robot's free motion and its interaction with the workpiece. To evaluate the performance of the proposed method, several experimental tests are performed and compared with other well-known methods. The results show the superiority of the proposed approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it