A Leader-Follower Trajectory Planning Approach for Cooperative Robotic System in Automated Fiber Placement
Bibliographic record
Abstract
The trajectory planning of cooperative robotic system for automated fiber placement (AFP) is a crucial topic since it has significant influence on the quality of the final products. This paper proposes a novel leader-follower trajectory planning approach for a 13-DOF (degree of freedom) cooperative robotic system in AFP. A 6-DOF serial robot holding the fiber placement head works as the leader, a 6-RSS (Revolute-Spherical-Spherical) parallel robot with a 1-DOF rotary stage holding the Y-shape mandrel works as the follower. Given a predefined fiber path with the desired angle 0°, an optimal trajectory planning method is developed for the serial robot subject to the kinematic and dynamic constraints. A cost function is established to guarantee the smoothness of the planned trajectory. Under this circumstance, the path of the fiber placement head may be deviated from the desired fiber path. According to the geometric constraint of AFP, namely, the direction of the roller in the fiber placement head should keep perpendicular to the mandrel surface, a vision-based trajectory generation strategy is designed for the parallel robot. Based on the desired trajectory of a defined point on the mandrel, the desired end-effector trajectory of the parallel robot can be determined using the visual measurement results. It can compensate the motion of the serial robot and ensure that the fiber is placed along the desired path. Simulation has been conducted to demonstrate the feasibility of the proposed leader-follower trajectory planning approach.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".