Planning collision-free and occlusion-free paths for industrial manipulators with eye-to-hand configuration
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Bibliographic record
Abstract
This paper presents a motion planning algorithm for industrial manipulators with the simultaneous constraints of avoiding collisions and avoiding the occlusion of specified pixellated regions of an eye-to-hand camera. The system uses a probabilistic roadmap to satisfy the constraints imposed by the command interface of typical industrial manipulators and uses dynamic collision checking to ensure collision-free motion. In the context of a task monitored by a camera, we enhance a probabilistic roadmap with a dynamic occlusion checking algorithm that is able to determine which pixels of the camera are occluded by the robot during each motion segment. The occlusion algorithm is formulated as collision algorithm where the field of view of the camera is represented as a quadtree of frustums. The proposed algorithm is demonstrated in industrial bin picking simulations where the gripper must not occlude the targeted object throughout the task.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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