A vision based online motion planning of robot manipulators
Bibliographic record
Abstract
This article presents a vision based online system for the robust trajectory planning of robot manipulators. It uses a 3D vision system to determine the relative position of the objects to be engaged and the obstacle to avoid, and a novel obstacle avoidance procedure for manipulator motion planning. From intensity images acquired by a CCD camera mounted on the robot arm, the salient features are first accurately and robustly detected and then grouped. Through the correspondences between the feature groupings and the model features, the 3D poses of the objects and the obstacles are determined and confirmed by back-projection. Once these poses are determined, an online procedure, based on redundancy resolution, is used to achieve obstacle avoidance. The approach utilizes a null space vector to set properly the robot configuration, and a potential field method to guide the end-effector. By pseudoinverse perturbation it also prevents singular configurations and local minima. The feasibility and effectiveness of the system is demonstrated by an experiment with online engagement and transportation of objects posed inside an aluminium frame.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".