Quasi-Real-time Autonomous Optimized Collision-free Robot Motion and Workpiece Setup Planning for Finishing Operations in a Cyber-Physical System
Bibliographic record
Abstract
In robotic finishing operations, the main challenge is to alleviate programming burdens and enhance accessibility for operators. Operators, often without programming skills, frequently need to redefine and modify finishing tasks due to variability in workpiece geometries, tool wear, and irregular features like weld spotters. This becomes more complicated when the operator must decide on redundant axes and part positions. Existing automation solutions often require highly skilled programmers and offline programming, which lead to delays and decreased productivity. This paper offers a novel approach for quasi-real-time autonomous planning of robot motion and workpiece setup. The objective is to propose an efficient and fast approach that optimizes finishing scenarios, including part setup and robot motions, with high success rates. The defined optimization problem is characterized by a non-linear, complex solution space with constraints like collision and joint limits, determining variables such as robot tool axisymmetry, external axes, and workpiece position and orientation. The objective function combines the robot condition number with the proximity of the home position to the task in the joint space. The proposed algorithm is seamlessly integrated within a ROS-based cyber-physical system and extensively validated across diverse test cases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".