Collaborative Research: Multi-Agent Adaptive Data Collection for Automated Post-Disaster Rapid Damage Assessment

协作研究：用于灾后自动化快速损害评估的多智能体自适应数据收集

• 批准号: 2316653
• 负责人: Mohammad Ilbeigi
• 金额: $ 19.04万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316653&HistoricalAwards=false
• 关键词: Collaborative; Research; Multi; Agent; Adaptive

In the immediate aftermath of a disaster, reconnaissance effort to identify building damage severity and distribution is critical for search and rescue and other time-sensitive decisions. However, existing data collection and analytical processes are less responsive to unforeseen and unexpected circumstances. Therefore, this project develops a novel adaptive data collection framework that constantly analyzes the most recent observations to determine and update the trajectory of data collector agents toward areas with the greatest potential for information gain. It enables these agents to collect reliable data under severe time and resource constraints. The outcomes of this project set the stage for automated damage assessment systems to improve the resilience of built environments and citizens in hazard-prone regions. A set of educational and outreach efforts are envisioned for broadly disseminating the research findings and integrating them into undergraduate and graduate courses.The adaptive data collection system is built on a novel hierarchical Bayesian framework for modeling disaster damage levels and a Bayesian optimization for adaptive destination identification and trajectory planning. This method first relies on a pre-disaster preliminary probabilistic model of physical damage levels for different types of structures at the census tract level of granularity using priori information and spatial attributes available to the public. It then creates and constantly updates distributions of physical damage levels across census tracts by integrating with the collected data from visited zones. Next, it dynamically and adaptively determines the trajectories for multiple agents to maximize the information gain in the shortest possible time. The Bayesian probabilistic models could be transferable to other complex problems such as environmental pollution assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在发生灾难之后，侦察的侦察努力对于搜索和救援和其他时间敏感的决定至关重要。但是，现有的数据收集和分析过程对不可预见和意外情况的反应较低。因此，该项目开发了一个新颖的自适应数据收集框架，该框架不断分析最新的观察结果，以确定和更新数据收集器代理的轨迹，以获得最大的信息增益潜力。它使这些代理能够在严重的时间和资源限制下收集可靠的数据。该项目的结果为自动损害评估系统奠定了基础，以改善易受危险区域的建筑环境和公民的弹性。设想了一系列教育和推广工作，以广泛传播研究结果并将其整合到本科和研究生课程中。自适应数据收集系统建立在一个新型的分层贝叶斯框架基于建模灾害损害水平的新型贝叶斯框架，用于自适应目的地识别和轨迹计划。该方法首先依赖于使用先验信息和空间属性可供公众使用的先验信息和粒度的不同类型结构的物理损害水平的预先概率模型。然后，它通过与访问区收集的数据集成，从而创建并不断更新人口普查区域的物理损害水平的分布。接下来，它动态和自适应地确定了多种代理的轨迹，以最大程度地提高信息增益。贝叶斯概率模型可以转移到其他复杂问题（例如环境污染评估）上。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来支持的。