D3ARC：异步协作多机器人系统的时限关键分布式灾害检测
D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems
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中文总结 AI 辅助
D3ARC提出异步分布式分层框架，通过多机器人协作感知与前瞻策略评估，在时限内高效可靠地检测野火，模拟中任务成功率达94%。
中文摘要 AI 辅助
气候变化正在加剧自然灾害的严重性和不可预测性。在野火等时限关键的危机中，传统监测手段仍受限于覆盖范围、成本和人员风险，这为自主和自适应监测解决方案开辟了道路。在此背景下，本文提出了D3ARC，一种用于时间感知和可靠野火检测的异步分布式分层框架。D3ARC整合了多个机器智能体，它们通过分布式感知、共享态势感知和协调行动在不确定性下进行合作。远程控制器异步地决定每个机器人的运动，而每个机器智能体感知环境并决定在何处以及如何执行野火检测。所有机器人操作都需要时间，随着时间推移，野火继续蔓延，减少了早期干预的机会。因此，所有智能体共享一个共同目标：在时间限制内尽可能快地以一定的性能阈值检测到野火。D3ARC整合了安全导航、覆盖效率、合作和可靠性机制。它引入了前瞻能力，使智能体能够通过在执行前评估候选策略来预测未来。该框架通过逼真的机器人模拟、消融研究和基线比较进行评估，实现了高达94%的总体任务成功率，检测置信度为89.4%。
英文摘要
Climate change is increasing the severity and unpredictability of natural disasters. In time-critical crises such as wildfires, traditional monitoring practices remain limited by coverage, cost, and personnel risk, paving the way for autonomous and adaptive monitoring solutions. Within this context, this paper introduces D3ARC, an asynchronous distributed hierarchical framework for time-aware and reliable wildfire detection. D3ARC integrates multiple robotic agents that cooperate under uncertainty through distributed perception, shared situational awareness and coordinated actions. A remote controller asynchronously decides upon each robot's motion, while each robotic agent senses the environment and decides where and how to execute the wildfire detection. All robotic operations require time, and as time progresses, wildfires continue to spread, reducing the opportunity for early intervention. As such, all agents share a common objective: to detect a wildfire with a certain performance threshold as fast as possible and within a time limit. D3ARC integrates mechanisms for safe navigation, coverage efficiency, cooperation and reliability. It introduces a forward-looking capability that allows agents to anticipate the future by evaluating candidate strategies before execution. The framework is evaluated through realistic robotics simulations, ablation studies, and baseline comparisons, achieving an overall mission success up to 94% with 89.4% detection confidence.
发表机构
- National and Kapodistrian University of Athens（雅典国立及卡波迪斯特里亚大学）
- Huawei Heisenberg Research Center（华为海森堡研究中心）
机构由 AI 辅助整理，请以论文原文为准。
