Quick Motion Path Plan for Joint-Robot to Pick Tomato under Free-Obstacle
Bibliographic record
Abstract
To improve the efficiency of picking the fruit, a kind of quick motion path plan method for fruit-picking robot under free-obstacle was proposed in this paper. Firstly, the method obtained a series of points along straight path at equal internal, and the gripper center was designed to pass thorough these points. Inverse kinematics formulas of the robot arm were used to solve joint angles of the robot arm when the gripper center will pass thorough each point. To make the robot arm guide the gripper to reach quickly the object point, the joint angles were optimized to determinate according to the principle of energy optimization. The test of picking tomato showed that the method can both reproduce the motion of the gripper along linear points at constant speed and keep the shortest motion path, which are benefit to grip the fruit steadily. The method has a low amount of calculation, better real-time and may provide a reference for joint robot to pick the fruit quickly.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".