Dec-MARVEL：预算约束下无通信的去中心化多智能体探索
Dec-MARVEL: Decentralized Multi-Agent Exploration without Communication under Budget Constraints
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中文总结 AI 辅助
针对多无人机探索受多种限制的问题，提出Dec-MARVEL框架，各机器人通过偶然观察协调，图注意力智能体融合多种特征选动作，经特定训练方式训练，在多场景试验中表现出色，实现模拟到现实的成功迁移与实际部署。
中文摘要 AI 辅助
多无人机探索常受不可靠通信、有限视野感知（如轻型机载摄像头）和有限行程预算限制，需各机器人预留足够预算返回基地。我们提出Dec-MARVEL，一种用于无通信且有方向感知团队的去中心化预算感知探索框架。各机器人通过偶然观察协调，图注意力智能体融合局部前沿几何、队友运动和预算特征来选择可行返回航点航向动作。通过特定训练方式训练智能体，在跨越三种团队规模和三种行程预算并与四个基线对比的900次保留试验中，Dec-MARVEL在所有团队规模预算配置中实现最高或并列最高探索率及最低感知重叠。在最紧720米预算下，不同规模机器人团队成功率高于最强基线。物理机器人实验证明了Dec-MARVEL从模拟到现实的成功迁移及实际部署。
英文摘要
Multi-UAV exploration is often constrained by unreliable communication, limited field-of-view sensing (e.g., lightweight onboard camera), and finite travel budgets that require each robot to reserve enough budget to return to its base. We present Dec-MARVEL, a decentralized budget-aware exploration framework for communication-free teams with directional sensing. Rather than exchanging maps, goals, or messages, each robot coordinates through its incidental observations: any teammate trajectory within its field of view serves as a coordination signal. A graph-attention actor fuses local frontier geometry, teammate motion, and budget features to select return-feasible waypoint-heading actions. The actor is trained with phase-conditioned critics, a training-only task-oriented privileged critic, and a mixture-based budget curriculum. Across 900 held-out trials spanning three team sizes (2, 4, 8 robots) and three travel budgets (720, 800, 1024 meters) against four baselines, Dec-MARVEL achieves the highest or tied-highest exploration rate and lowest sensing overlap across all nine team-size budget configurations. Under our tightest 720m budget, it reaches 53%, 94%, and 100% success for 2, 4, and 8 robots, versus 37%, 83%, and 99% for the strongest baseline. Physical-robot experiments demonstrate successful sim-to-real transfer and real-world deployment of Dec-MARVEL.
发表机构
- Sogang University（西江大学）
- National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
