CAREER: Autonomous Live Sketching of Dynamic Environments by Exploiting Spatiotemporal Variations

有许多紧急情况需要人类立即了解环境。例如，在化学或核泄漏灾难发生后，需要立即确定污染物的扩散和强度，以获得最佳的第一反应。艺术实景素描需要快速捕捉瞬变场景并揭示其最显著的时空特征，受此启发，配备先进人工智能(AI)算法的自主移动机器人将通过自主污染物采样和实时环境建模，对高度动态的环境进行实景素描。这项研究的成功可能会改变极端条件下自动环境监测的游戏规则。结果自然可以扩展到许多应用，包括那些非灾难性但时间关键的情景，如烟雾污染和藻类水华监测。教育目标是通过将这项研究整合到课程开发中，引导学生参与研究(特别是女性和少数族裔学生)以及社区外展，促进人们对机器人职业的普遍兴趣。本项目的研究目标是通过机器人车辆的自适应环境采样，调查原则性、快速和精确的环境建模技术。这一研究计划解决了从重要的人工智能-机器人领域提取的各种必要技术，包括数据驱动建模、采样轨迹规划、不确定情况下的决策以及多机器人协调。一个重要的目标是对所有这些子领域及其联系有更深入的了解，从而设计出一个有原则的全面框架，以建立一个完整的综合系统。将开发一套相互依赖的建模和优化方法的新解决方案，以便即使通过使用受非常短的数据收集时间窗口约束的少量样本，也可以高精度地学习潜在环境模型及其时空变化(例如，污染物分布和扩散过程)。建议的工作包括开发理论结果和算法，但也强调它们在各种具有挑战性的和非结构化环境中的应用。该项目由跨部门的机器人基础研究计划支持，该计划由工程总监(ENG)和计算机和信息科学与工程(CEISE)共同管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

There are many emergency scenarios that require humans to understand the environments instantly. For example, after disasters of chemical or nuclear leakage, the spread and intensity of the contaminant need to be characterized immediately for the best first response. Inspired by artistic live sketching which needs to rapidly capture transient scenes and unveil their most salient spatiotemporal characteristics, the autonomous mobile robots equipped with advanced artificial intelligence (AI) algorithms will be utilized to "live sketch" highly dynamic environments through autonomous contaminant sampling and real-time environmental modeling. Success of this research could potentially be a game-changer for automated environmental monitoring under extreme conditions. The results can be naturally extended to many applications including those non-disastrous but time-critical scenarios such as smog pollution and algal bloom monitoring. The education objective is to promote general interest in robotics careers by integrating this research into curriculum development, direct student involvement in research (particularly for female and minority students), as well as community outreach.The research objective of this project is to investigate principled, expeditious, and precise environmental modeling techniques through adaptive environmental sampling with robotic vehicles. This research program tackles a variety of needed techniques drawn from important AI-robotics subfields including data-driven modeling, sampling trajectory planning, decision making under uncertainty, as well as multi-robot coordination. An important goal is to obtain deeper insights into all these subfields and their connections, leading to a design of a principled and comprehensive framework for building a complete integrated system. New solutions of a set of inter-dependent modeling and optimization methods will be developed, so that the latent environment model and its spatiotemporal variations (e.g., contamination distribution and diffusion processes) can be learned with high accuracy, even through using a small number of samples constrained by a very short data-collection time window. The proposed efforts include development of theoretical results and algorithms, but also emphasize their applications in various challenging and unstructured environments.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.