RI: Small: Exploiting Symmetries of Decision-Theoretic Planning for Autonomous Vehicles

RI：小：利用自动驾驶车辆决策理论规划的对称性

• 批准号: 2006886
• 负责人: Lantao Liu
• 金额: $ 45万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006886&HistoricalAwards=false
• 关键词: RI; Small; Exploiting; Symmetries; Decision

The project aims at making breakthroughs in AI planning theories that directly affect the accuracy and efficiency of autonomous robots' basic motion and coordination. These advances will play an important role for enhancing the systems' resilience to extreme disturbances in unstructured environments. Specifically, an important focus of this research is to gain a deeper understanding of the interrelationship between model "symmetries" and various performance characteristics in decision-theoretic planning. Traditionally, study of model symmetries belongs to the field of group theory. This research is based on the intuition that autonomous systems acting in real-world environments follow physics laws, and many systems studied in physics show some form of symmetry. Model symmetries in planning, decision-making, and multi-agent coordination domains can be immensely beneficial for designing efficient solving mechanisms to tackle problems with large computational and communication complexities. The results of this project will enhance the autonomy and intelligence of many mobile robot platforms that perform challenging outdoor missions such as environmental monitoring and surveillance, search and rescue, and other applications of societal importance. This research re-thinks the modeling methods of decision-theoretic planning by examining an important class of group-theoretic mechanisms and the possibilities for utilizing them to boost existing solutions to many planning, decision making and coordination problems. Identifying the symmetric properties during the modeling process will help reduce search space and minimize the redundancy that often leads to drastic inefficiencies in computation and communication. By carefully exploiting the symmetric structures of not only the continuous vehicle motion transformation, but also the discrete vehicle state transition, principled frameworks that synthesize various elegant symmetries will be designed, and new models, theoretical analyses, as well as implementation algorithms will be developed. The project will seek improvements related to algorithm convergence, solution quality/accuracy, uncertainty tractability, and system distributability (for multi-robot systems).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.

该项目旨在在AI规划理论中取得突破，直接影响自动机器人基本运动和协调的准确性和效率。这些进步将在增强系统对非结构化环境中极端干扰的韧性方面发挥重要作用。具体而言，这项研究的一个重要重点是对模型“对称性”与决策理论计划中各种绩效特征之间的相互关系有更深入的了解。传统上，模型对称性的研究属于群体理论领域。这项研究基于直觉，即在现实环境中起作用的自主系统遵循物理定律，许多在物理学中研究的系统都表现出某种形式的对称性。在计划，决策和多机构协调域中进行的模型对称性对于设计有效的解决机制以解决具有较大的计算和通信复杂性问题的问题非常有益。该项目的结果将增强许多移动机器人平台的自主权和智能，这些平台执行了挑战性的户外任务，例如环境监测和监视，搜索和救援以及其他社会重要性的应用。这项研究通过检查一类重要的群体理论机制以及利用它们来提高现有解决方案来解决许多计划，决策和协调问题的可能性，从而重新考虑了决策理论计划的建模方法。在建模过程中识别对称属性将有助于减少搜索空间，并最大程度地减少冗余，这通常会导致计算和通信中急剧低差。通过仔细利用不仅连续车辆运动转换的对称结构，还可以设计离散的车辆状态过渡，合成各种优雅对称性的原则框架，并将开发新的模型，理论分析以及实现算法。该项目将寻求与算法融合，解决方案质量/准确性，不确定性障碍和系统分配性有关的改进（对于多机器人系统）。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来获得支持的。

项目成果

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

Information-Driven Path Planning

• DOI: 10.1007/s43154-021-00045-6
• 发表时间: 2021-04
• 期刊: Current Robotics Reports
• 作者: Shi Bai;Tixiao Shan;Fanfei Chen;Lantao Liu;Brendan Englot

Efficient Map Prediction via Low-Rank Matrix Completion

• DOI: 10.1109/icra48506.2021.9561353
• 发表时间: 2021-05
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Zheng Chen;Shi Bai;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