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

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深入了解决策理论规划中模型“对称性”与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。这项研究基于这样一种直觉，即在现实环境中行动的自主系统遵循物理定律，而许多在物理学中研究的系统都表现出某种形式的对称性。在规划、决策和多智能体协调领域中的模型对称性对于设计有效的解决机制来解决具有大量计算和通信复杂性的问题非常有益。该项目的成果将增强许多移动机器人平台的自主性和智能，这些平台可以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。本研究通过考察一类重要的群论机制，重新思考决策理论规划的建模方法，以及利用它们来促进许多规划、决策和协调问题的现有解决方案的可能性。在建模过程中识别对称属性将有助于减少搜索空间和最小化冗余，而冗余通常会导致计算和通信效率的严重低下。通过仔细研究连续车辆运动变换和离散车辆状态转换的对称结构，将设计出综合各种优雅对称性的原则框架，并开发新的模型、理论分析和实现算法。该项目将寻求与算法收敛、解决方案质量/准确性、不确定性可追溯性和系统可分发性（多机器人系统）相关的改进。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。

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.