Recursive Neural Network Optimization Strategies for Multi-Target Detection and Path Planning in Autonomous Systems for Multi-Intelligent Collaboration
Bibliographic record
Abstract
The article applies recurrent neural networks to multi-intelligent body collaborative autonomous systems and uses optimized RNN algorithms for multi-objective detection and path planning of intelligent bodies.The multi-intelligent body multi-target detection and path planning model optimized based on recurrent neural network is constructed to realize multi-target detection and tracking of intelligent bodies and multi-intelligent collaborative path planning.Simulation experiments are designed with a mobile robot as the research object to analyze the trajectory tracking and path planning effects of the multi-target detection and path planning model in this paper.The error between the actual trajectory and the reference position of the robot trajectory tracking is continuously reduced, and reaches complete coincidence at the 127th reference tracking point.The actual speed and acceleration errors of the robot are infinitely close to 0. The accuracy of this paper's algorithm in multi-objective path planning is 100%, the average arrival time is 20.02s, and the probability of collision is 0%, which is much better than other algorithms.The algorithm in this paper has the highest path smoothing validity for planning in three environments.In the 30 × 83 warehouse map, the total path length of this paper's algorithm is shortened by 13.00% and 10.77%, and the total path cost is shortened by 9.71% and 11.52% compared with the Wd-SIPP algorithm for the number of collaborative robots in a single group of three and five, respectively.In 100*100 storage map, the total path length is shortened by 10.32% and 11.67%, and the total path cost is shortened by 7.34% and 12.09%, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".