Path Smoothing Optimization for Mobile Robots in Intelligent Chemistry Laboratory Based on Improved A* Algorithm
Bibliographic record
Abstract
To meet the high requirements for pipetting stability in smart chemical laboratory mobile robots, this paper proposes a path smoothing optimization method based on an improved A* algorithm. The method introduces bidirectional jump point search technology, which significantly reduces the search space by initiating simultaneous searches from both the start and end points. Additionally, to address issues of excessive path turns and redundancy, the Floyd algorithm is integrated to optimize the path, while an enhanced evaluation function further improves search accuracy and efficiency. Simulation results using MATLAB demonstrate that the improved A* algorithm achieves notable improvements in runtime, number of traversed nodes, path turning points, and path smoothness. Under the premise of ensuring path planning efficiency, the method satisfies the special smoothness requirements for mobile composite robot path planning in smart chemical laboratories. This study provides robust technical support for the automation development of smart chemical laboratories and advances overall operational efficiency to a higher level.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".