Adaptive Multi-Robot Exploration for Unknown Environments Using Edge-Weighted Path Planning
Bibliographic record
Abstract
Efficient multi-robot exploration of unknown environments is critical for numerous applications such as search and rescue, planetary exploration, and environmental monitoring. Existing centralized approaches struggle with scalability, while decentralized methods often incur high computational costs and inefficient task coordination. This study presents a scalable and adaptive multi-robot exploration algorithm that adaptively updates edge weights based on visit counts, reservations, and obstacles to optimize path allocation and minimize redundant scanning. The proposed algorithm ensures 100% area coverage and real-time adaptability, making it robust for exploration in many different unknown environments. The algorithm was validated in both a 2D grid-based simulation and a high-fidelity 3D environment using Isaac Sim with ROS integration. Experimental results demonstrate that the algorithm achieves improved exploration efficiency and adaptability compared to a real-time scheduling method while maintaining computational feasibility. The findings highlight the effectiveness of edge-weighting and reservation-based task allocation strategies for autonomous multi-robot systems in practical exploration scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".