Research on collaborative scheduling method for multi-robot tomato picking based on improved particle swarm optimization algorithm
Bibliographic record
Abstract
Currently, multi-robot cooperative algorithms are widely used in the field of agriculture, which greatly improves the efficiency of agricultural production. However, the multi-robot cooperative operation of agricultural machinery is mostly limited to the efficiency and accuracy of scheduling. To address the mentioned shortcomings, a novel multi-task scheduling method based on improved particle swarm optimization algorithm is proposed, which is applied to the efficient collaborative scheduling problem of tomato picking robots and transfer vehicles in greenhouse cultivation of different scales. Firstly, the scene of collaborative scheduling between tomato automatic picking and transshipment is described, and the mathematical model of multi-machine collaborative scheduling is established with the shortest waiting time of picking robots and the minimum number of transshipment vehicles as the optimization objectives. Secondly, an improved particle swarm optimization algorithm is expounded in detail, which customizes the fitness function and enhances the particle update strategy. Finally, the experimental results show that the improved particle swarm optimization algorithm can not only determine the optimal number and execution order of cooperative robots, but also reduce the task execution time by 47% compared with the unimproved method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".