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

在灾难发生后，立即进行勘察，以确定建筑物损坏的严重程度和分布是至关重要的搜索和救援和其他时间敏感的决策。然而，现有的数据收集和分析程序对不可预见和意外的情况反应不快。因此，该项目开发了一种新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，以获得最大的信息潜力。它使这些代理能够在严格的时间和资源限制下收集可靠的数据。该项目的成果为自动化损害评估系统奠定了基础，以提高灾害易发地区建筑环境和公民的复原力。一套教育和推广工作的设想，广泛传播的研究成果，并将其纳入本科和研究生courses.The自适应数据收集系统是建立在一个新的层次贝叶斯框架建模灾害损害水平和贝叶斯优化的自适应目的地识别和轨迹规划。该方法首先依赖于灾害前的初步概率模型的物理损坏程度的不同类型的结构，在人口普查区的粒度水平，使用先验信息和空间属性提供给公众。然后，它通过与从访问区收集的数据相结合，创建并不断更新整个人口普查区的物理破坏水平分布。接下来，它动态地和自适应地确定多个代理的轨迹，以在最短的时间内最大限度地提高信息增益。贝叶斯概率模型可以转移到其他复杂的问题，如环境污染评估。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的知识价值和更广泛的影响审查标准。

In the immediate aftermath of a disaster, reconnaissance efforts to identify building damage severity and distribution are 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 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 a 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 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.