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，在发生化学或核泄漏灾难后，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突出的时空特征的启发，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来“现场素描”高动态环境。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。结果可以自然地扩展到许多应用，包括那些非灾难性但时间关键的场景，如雾霾污染和藻华监测。教育目标是通过将这项研究纳入课程开发，学生直接参与研究（特别是女性和少数民族学生）以及社区外展，促进对机器人职业的普遍兴趣。该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有原则的、快速的和精确的环境建模技术。该研究项目涉及从重要的人工智能机器人子领域中提取的各种必要技术，包括数据驱动建模、采样轨迹规划、不确定性下的决策制定以及多机器人协调。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，从而设计出一个原则性的、全面的框架，以构建一个完整的集成系统。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限制的少量样本，也可以高精度地学习潜在环境模型及其时空变化（例如，污染分布和扩散过程）。提出的努力包括理论结果和算法的发展，但也强调它们在各种具有挑战性和非结构化环境中的应用。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，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。

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

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

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