S&AS: INT: COLLAB: Autonomy as a Service

如何在长时间内部署自主机器人团队，以便操作员可以招募和分配任务，执行各种各样的任务？这类任务的例子包括在生物保护应用或精准农业中遇到的环境监测任务。这个项目将解决这个问题，通过一个新颖的“自治即服务”框架，让自主机器人以按需的方式提供给用户。为了实现这一想法，将开发新的工具来(i)以机器人可以理解的方式描述任务，（ii）确保机器人在执行任务时保持安全，以及（iii）机器人随着时间的推移学习和改进的方法，并结合评估其性能的能力。该项目的更广泛影响将包括对环境监测的影响，增加STEM参与的外展计划，以及将研究成果整合到三所参与机构（佐治亚理工学院、波士顿大学和麻省理工学院）的课程中。详细地说，三个主要的研究主题是：(i)从规范到执行：用户必须能够招募和分配机器人新的任务，这需要正式正确的方式，从高层次的规范，制定为线性时间逻辑公式，协调机器人执行的控制程序。（ii）弹性自治：当交付一个可以长时间执行任务的系统时，首先要考虑的必须是保持系统本身的完整性，即即使机器人团队被招募来执行一组特定的任务，也必须确保基本功能。该项目将通过使用可组合的屏障证书来实现这一点，该证书确保安全集的前向不变性，即，如果机器人安全启动，它们将保持安全。（iii）基于轨迹的海量数据集学习：代理团队必须能够评估他们正在监控的任何东西的性能。在这个项目中，这将通过模型来实现，这些模型可以通过新颖的数据压缩和表示工具从大量数据集中有效地学习。

How can one deploy teams of autonomous robots over long periods of time in such a way that they can be recruited and tasked by operators to perform a wide variety of tasks? Examples of such tasks include the environmental monitoring tasks encountered in biological conservation applications or in precision agriculture. This project will address this issue by letting the autonomous robots be available to the user in an on-demand manner through a novel 'Autonomy as a service' framework. To realize this idea, new tools will be developed for (i) describing the tasks in a way that can be understood by the robots, (ii) ensuring that the robots stay safe while executing the tasks, and (iii) methods for the robots to learn and improve over time in combination with the ability to assess their performance. The broader impact from the project will include implications for environmental monitoring, outreach programs for increasing STEM participation, and an integration of the research findings into the curriculum at the three participating institutions (Georgia Tech, BU, and MIT). In detail, the three main research themes are: (i) From Specification to Execution: The users must be able to recruit and task the robots with new missions, which calls for formally correct ways of going from high-level specifications, formulated as Linear Temporal Logic formulae, to coordinated control programs for the robots to execute. (ii) Resilient Autonomy: When delivering a system that can be commanded to perform tasks over long periods of time, the first concern must be to preserve the integrity of the system itself, i.e., basic functionality must be ensured even as the robot team is recruited to perform a particular set of tasks. This project will achieve this through the use of composable barrier certificates that ensure the forward invariance of the safe set, i.e., if the robots start safe, they will stay safe. (iii) Trajectory Based Learning from Massive Data Sets: The agent team must be able to assess the performance of whatever it is that they are monitoring. In this project, this will be achieved through models that can be effectively learned from massive data sets through novel tools for data compression and representation.