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适应能力方面发挥重要作用。具体地说，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绩效特征之间的相互关系。传统上，模型对称性的研究属于群论领域。这项研究是基于这样一种直觉，即在现实世界环境中行为的自治系统遵循物理定律，许多物理学研究的系统表现出某种形式的对称性。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处理具有较大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性，这些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。这项研究通过考察一类重要的群论机制以及利用它们来改进许多规划、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，而冗余往往导致计算和通信的严重低效。通过仔细利用连续车辆运动变换和离散车辆状态变换的对称结构，将设计合成各种优雅对称的原则性框架，并开发新的模型、理论分析和实现算法。该项目将寻求在算法收敛、解决方案质量/精度、不确定性可处理性和系统可分布性(针对多机器人系统)方面的改进。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.