Research on path planning of patrol robot based on multi-algorithm fusion
Bibliographic record
Abstract
A multi-algorithm fusion path planning algorithm for patrol robots was proposed, In order to improve the robot's path planning ability, optimize the search efficiency, improve the robot's path smoothness and improve the control accuracy. The A* algorithm is optimized through the search field and heuristic function to optimize the node search, avoid the expansion of redundant nodes and improve the search efficiency of the algorithm while ensuring the optimal global path. The improved A* algorithm still has node redundancy, excessive path transition and other phenomena. Floyd algorithm is used to introduce improved A* key nodes to optimize the improved A* algorithm again, eliminate redundant nodes, smooth the global path, and dynamically increase the number of key nodes for long-distance key nodes to effectively prevent path deviation. In view of the shortcomings of the improved A* algorithm in dynamic obstacle planning, the improved DWA algorithm is integrated to achieve local path planning, and the integrated path planning algorithm has local dynamic and unknown environment obstacle avoidance ability. Experiments show that the proposed fusion algorithm has the ability of global path planning and local path planning, which verifies the feasibility and effectiveness of the fusion algorithm.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".