Optimal Image-Based Task-Sequence/Path Planning and Robust Hybrid Vision/Force Control of Industrial Robots
Bibliographic record
Abstract
In this paper, a novel image-based task-sequence/path planning scheme coupled with a robust vision/force control method is suggested for solving the multi-task operation problem of an eye-in-hand (EIH) industrial robot interacting with a workpiece. The proposed method suggests an optimal task sequence planning scheme to perform all the tasks and an optimal path planning method to generate a collision-free path between the tasks when the robot performs free-motion. To this end, a new method is presented which solves both problems simultaneously. A novel deadlock-free modified artificial potential field (MAPF) based on rotational potential force is developed for generating the collision-free path betweentasks in the three-dimensional (3D) environment. The parameters of the MAPF and the sequence of the tasks are found by an optimizer simultaneously. This problem can be considered as a MAPF-constrained-generalized-traveling-salesman-problem (MAPF-CGTSP), which is a mix-integer optimization problem. The mix-integer version of multi-tracker optimization algorithm (MTOA) is developed to solve the problem. However, since image-based visual servoing (IBVS) is used for motion control, the planning is conducted in the image space. Integrated with the proposed planning method, a novel chattering-free filtered quasi sliding mode controller (FQSMC) is specially designed for robust vision/force control of the robot. FQSMC exploits a novel variable-gain orthogonal-sliding-manifold (VGOSM)which enables the robot to switch between free-motion mode and interaction mode. FQSMC overcomes large uncertainties and filters out the existing noises by exploiting an intrinsic filter within its control law. Experimental results show the superiority of the proposed approach to other state-of-the-art methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".