Adaptable and Robust Multi-Robot Decision Making through Generalized Sequential Stochastic Task Assignment

该项目设想由多个机器人系统组成的团队协作并自主地在物理世界中执行复杂的任务。这些任务包括环境监测、搜救和科学探索，机器人的任务是向最终用户提供及时的信息。许多机器人任务的成功取决于少数关键的、不可逆转的和高影响的决定。这种决策的例子包括从地面机器人选择在哪里部署空中机器人，确定如何部署通信硬件，以及指定何时执行特定的运动行为。在目前的现场机器人系统中，这些类型的决策在很大程度上留给了人类操作员，他们通常没有必要的情境意识、推理技能或可用时间来有效地做出这些决策。这项工作旨在弥合当前需要大量人力投入才能做出高影响力决策的多机器人系统与未来能够在正确的时间和地点执行最有效行为的智能机器人系统之间的差距。该项目的最终目标是开发新的算法解决方案，用于在异质多机器人团队中做出高影响力的决策。在对这样的决策进行推理时，必须考虑许多变量，例如任务目标、可用的动作、环境信念模型、未来的奖励以及其他机器人的行为和能力。这些变量中的许多都带有很大程度的不确定性，事先就相信它们的值，并且可能会根据动态条件和机器人的观察而变化。通过建立和求解序列随机分配问题(SSAP)的新的推广，将开发用于对该信息进行推理的新的算法解决方案。这些SSAP推广需要对机器人动作的不确定的未来值进行推理，考虑到在现场获得的信息，利用奖励分布中的依赖关系，并以分散的方式计算策略。将通过海洋监测和环境勘探情景的模拟和现场实验进行验证。开发的算法将通过开源分发向公众开放，并将有助于促进与海洋和环境科学家的持续合作。该项目由跨部门机器人基础研究计划支持，该计划由工程指导委员会(ENG)和计算机和信息科学与工程指导委员会(CEISE)联合管理和资助。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。

This project envisions teams of multiple robotic systems cooperatively and autonomously executing complex missions in the physical world. These missions include environmental monitoring, search and rescue, and scientific exploration, where robots are tasked to provide timely information to end users. The success of many robotic missions depends on a small number of critical, irreversible, and high-impact decisions. Examples of such decisions include selecting where to deploy aerial robots from ground robots, determining how to deploy communication hardware, and specifying when to execute specific motion behaviors. In current fielded robotic systems, these types of decisions are largely left to human operators, who typically do not have the required situational awareness, reasoning skills, or available time to make these decisions effectively. This work seeks to bridge the gap between current multi-robot systems that require significant human input to make high-impact decisions and future intelligent robotic systems capable of executing the most effective behaviors at the right time and location.The ultimate objective of this project is to develop new algorithmic solutions for making high-impact decisions in heterogeneous multi-robot teams. When reasoning over such decisions, many variables must be considered, such as the mission goals, available actions, environment belief models, future rewards, and the behaviors and capabilities of other robots. Many of these variables carry a significant degree of uncertainty, have a prior belief of their value, and may change based on dynamic conditions and robot observations. New algorithmic solutions for reasoning over this information will be developed by formulating and solving new generalizations of the sequential stochastic assignment problem (SSAP). These SSAP generalizations require reasoning over the uncertain future values of robot actions, accounting for information acquired in situ, exploiting dependencies in the reward distributions, and computing policies in a decentralized manner. Validation will be performed through both simulated and field experiments for marine monitoring and environment exploration scenarios. The developed algorithms will be made publicly available through open source distribution and will help foster ongoing collaborations with marine and environmental scientists.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.