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，在发生化学品或核泄漏灾害后，需要立即确定污染物的扩散和强度，以便作出最佳的第一反应。受艺术现场素描需要快速捕捉瞬态场景并揭示其最显著的时空特征的启发，配备先进人工智能（AI）算法的自主移动的机器人将通过自主污染物采样和实时环境建模来“现场素描”高度动态的环境。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。研究结果可以自然地扩展到许多应用，包括那些非灾难性的，但时间紧迫的情况下，如烟雾污染和藻类水华监测。教育目标是通过将这项研究融入课程开发，直接参与研究的学生（特别是女性和少数民族学生），以及社区outreach.本项目的研究目标是通过机器人车辆的自适应环境采样来研究原则，快速和精确的环境建模技术，以促进对机器人职业的普遍兴趣。该研究计划解决了从重要的人工智能机器人子领域中提取的各种所需技术，包括数据驱动建模，采样轨迹规划，不确定性下的决策以及多机器人协调。一个重要的目标是更深入地了解所有这些子领域及其联系，从而设计一个原则性和全面的框架，以构建一个完整的集成系统。将开发一组相互依赖的建模和优化方法的新解决方案，使得潜在环境模型及其时空变化（例如，污染物分布和扩散过程）可以高精度地学习，甚至通过使用受非常短的数据收集时间窗口约束的少量样本。建议的努力包括理论结果和算法的发展，但也强调其在各种具有挑战性的和非结构化的环境中的应用。由工程局（ENG）和计算机与信息科学与工程局（CISE）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知识价值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.