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

职业：利用时空变化自主实时绘制动态环境草图

• 批准号: 2047169
• 负责人: Lantao Liu
• 金额: $ 54.99万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047169&HistoricalAwards=false
• 关键词: CAREER; Autonomous; Live; Sketching; Dynamic

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.

有许多紧急情况需要人类立即了解环境。例如，在化学或核泄漏的灾难发生后，需要立即将污染物的扩散和强度表征为最佳的第一反应。受艺术现场素描的启发，需要快速捕获短暂的场景并揭示其最突出的时空特征，配备了高级人工智能（AI）算法的自主移动机器人将通过自动污染物采样和实时环境建模来“实时人工智能（AI）算法。这项研究的成功可能是在极端条件下自动化环境监测的游戏改变者。结果自然可以扩展到许多应用程序，包括那些非疾病但时间关键时期的情况，例如烟雾污染和藻类绽放监测。教育目标是通过将这项研究纳入课程开发，直接参与研究（尤其是女性和少数族裔学生）以及社区外展的方式来促进对机器人职业的普遍兴趣。该项目的研究目标是通过适应性的环境采样来调查原理，快速和精确的环境建模技术。该研究计划涉及从重要的AI-机器人子领域提取的各种需要的技术，包括数据驱动的建模，采样轨迹计划，不确定性下的决策以及多机器人协调。一个重要的目标是获得对所有这些子领域及其联系的更深入的见解，从而设计出一个有原则且全面的框架来构建完整的集成系统。将开发一组相互依赖的建模和优化方法的新解决方案，以便可以高精度地学习潜在环境模型及其时空变化（例如，污染分布和扩散过程），即使使用少数样品受到非常短的数据收集时间窗口的约束。拟议的努力包括发展理论结果和算法，同时也强调了它们在各种具有挑战性和非结构化环境中的应用。该项目得到了机器人计划中的跨领域基础研究的支持，共同管理和资助了由工程局（ENG）和计算机和信息科学和工程（CISE）的宣布（CISE）。基金会的智力优点和更广泛的影响评论标准。

项目成果

Decision-Making Among Bounded Rational Agents

有限理性主体之间的决策

• 发表时间: 2022
• 期刊: International Symposium on Distributed Autonomous Robotic Systems (DARS
• 作者: Xu, Junhong;Pushp, Durgakant;Yin, Kai;Liu, Lantao
• 通讯作者: Liu, Lantao

AK: Attentive Kernel for Information Gathering

• DOI: 10.48550/arxiv.2205.06426
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Weizhe (Wesley) Chen;R. Khardon;Lantao Liu

Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy

未知杂乱环境中 AGV 的自主导航：log-MPPI 控制策略

• DOI: 10.1109/lra.2022.3192772
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters (RA-L
• 作者: Mohamed, Ihab;Yin, Kai;Liu, Lantao
• 通讯作者: Liu, Lantao

Model-Agnostic Multi-Agent Perception Framework

• DOI: 10.1109/icra48891.2023.10161460
• 发表时间: 2022-03
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Weizhe (Wesley) Chen;Runsheng Xu;Hao Xiang;Lantao Liu;Jiaqi Ma

Informative Planning in the Presence of Outliers

• DOI: 10.1109/icra46639.2022.9812267
• 发表时间: 2021-11
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Weizhe (Wesley) Chen;Lantao Liu