An Adaptive Coordination Exploration Approach for Multi-UAV Based on Entropy-Guided Local Planning
Bibliographic record
Abstract
Autonomous exploration in unknown environments is a critical capability for multi-UAV systems. However, existing methods often suffer from unbalanced task allocation, low exploration efficiency, and unstable paths, especially in large-scale and complex scenarios. To address these challenges, this paper presents an adaptive coordination exploration approach for multi-UAV systems. In the proposed approach, dynamic region allocation, entropy-guided local planning, and direction-consistent frontier selection are integrated to achieve efficient and collaborative exploration. The system first partitions the environment adaptively based on workload and regional complexity. It then prioritizes high-information-value areas for exploration. Directional constraints are further applied to improve path continuity and reduce turning redundancy. Extensive experiments in indoor maze and pillar environments, and outdoor forest and urban environments demonstrate that the proposed approach outperforms state-of-the-art baselines. Furthermore, ablation studies validate the necessity and complementarity of each module. This work provides a practical and efficient solution for multi-UAV exploration in structured and unstructured environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".