Optimal Path Generation and Real-time Scheduling for Autonomous Mobile Platforms
Bibliographic record
Abstract
Optimizing path planning and real-time scheduling are crucial for autonomous mobile vehicles under dynamic conditions. In this study, advanced algorithms are developed to achieve the above functions under breakdown scenarios for autonomous sweeping trucks with operational constraints. The methodology involves Spectral clustering, utilizing its ability to efficiently assign service routes to a predefined number of vehicles. Simulated Annealing (SA) in conjunction with the Traveling Salesman Problem (TSP) is then employed to systematically optimize route sequences, ensuring minimal travel distances and efficient coverage. Real-time scheduling functions dynamically redistribute routes using a comprehensive approach tailored to breakdown disruptions, ensuring operational efficiency. Edge redistribution employs Kernighan-Lin bisection to equally allocate remaining unattended road edges from broken vehicles to operational ones, promoting an equitable distribution of tasks among available resources. Simultaneously, Dijkstra’s algorithm is applied to identify the shortest path between the last edge of a working vehicle and the first redistributed one, minimizing travel distances and optimizing route sequences. Reconnection strategies are implemented to eliminate any disconnected edges resulting from the redistribution process. Simulations demonstrate the developed algorithms’ effectiveness, providing a 71.6% efficiency rate and a short computation time of 35.63 seconds for a 17.68 km service route. Additionally, detailed statistical calculations include normal service distance, deadhead distance, and overall efficiency for both normal operation and breakdown scenarios. The algorithms can enhance work efficiency and resource utilization for various autonomous mobile systems through adaptive path planning and real-time scheduling based on operational conditions and demand.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".