Stiffness optimization for two‐armed robotic sculpting
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Recent research has considered robotic machining as a dextrous alternative to traditional CNC machine tools for complex sculptured surfaces. One challenge in using robotic machining is that the stiffness is lower than traditional machine tools, due to the cantilever design of the links and low‐torsional stiffness of the actuators. This paper seeks to examine this limitation, using optimization algorithms to determine the best trajectories for the manipulators such that the stiffness is maximized. Design/methodology/approach The issue of low stiffness is addressed with an integrated off‐line planner and real‐time re‐planner. The available manipulator stiffness is maximized during off‐line planning through a trajectory resolution method that exploits the nullspace of the robot machining system. In response to unmodeled disturbances, a real‐time trajectory re‐planner utilizes a time‐scaling method to reduce the tool speed, thereby reducing the demand on the actuator torques, increasing the robot's dynamic stiffness capabilities. During real‐time re‐planning, priorities are assigned to conflicting performance criteria such as stiffness, collision avoidance, and joint limits. Findings The algorithms developed were able to generate trajectories with stiffer configurations, which resulted in a reduction in the actuator torques. The real‐time re‐planner successfully allowed the process plan to continue when disturbances were encountered. Research limitations/implications Simulations are presented to demonstrate the effectiveness of the approach. Practical implications Addressing the limitation of stiffness in serial‐link manipulators will enable robots to become more suitable for machining tasks. The real‐time re‐planning approach will allow robots to become more autonomous during the execution of a given task. Originality/value An integrated off‐line and real‐time planning approach has been applied to robotic machining.
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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 | 0.001 | 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 | 0.000 |
| 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