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.

该项目旨在突破人工智能规划理论，直接影响自主机器人基本运动和协调的准确性和效率。这些进展将在增强系统对非结构化环境中极端干扰的恢复能力方面发挥重要作用。具体而言，本研究的一个重要重点是更深入地了解模型“对称性”和决策理论规划中的各种性能特征之间的相互关系。传统上，模型对称性的研究属于群论的范畴。这项研究是基于这样一种直觉，即在现实世界环境中作用的自治系统遵循物理定律，并且物理学中研究的许多系统都表现出某种形式的对称性。在规划，决策和多代理协调领域的模型对称性可以是非常有益的设计有效的解决机制，以解决问题的计算和通信的复杂性大。该项目的成果将提高许多移动的机器人平台的自主性和智能性，这些机器人平台执行具有挑战性的户外任务，如环境监测和监视、搜索和救援以及其他具有社会重要性的应用。这项研究通过检查一类重要的群论机制以及利用它们来增强许多规划、决策和协调问题的现有解决方案的可能性，重新思考决策理论规划的建模方法。在建模过程中识别对称属性将有助于减少搜索空间，并最大限度地减少冗余，这通常会导致计算和通信效率低下。通过仔细利用对称结构，不仅连续的车辆运动变换，而且离散的车辆状态转换，原则性的框架，综合各种优雅的对称性将被设计，新的模型，理论分析，以及实现算法将被开发。该项目将寻求与算法收敛性、解决方案质量/准确性、不确定性易处理性和系统可分布性（多机器人系统）相关的改进。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。

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