RI: Small: Robust Autonomy for Uncertain Systems using Randomized Trees

近年来，一场真正的革命正在发生，智能机器和机器人在以前从未见过的新环境中操作，并与人类操作员互动。过去，机器人主要在工业环境中找到，而现在，自主和半自动的机器人和系统几乎随处可见。这一新一代智能自主系统将更紧密地与人类互动，并将帮助他们在日常生活中，无论是工作、休闲，还是通过照顾许多平凡的家务。但世界是一个混乱的地方。机器人在工厂车间的封闭“笼子”里一遍又一遍地重复同样的任务，与需要在办公室环境、医院或高速公路上导航的机器人之间存在巨大差异，在这些环境中，不确定性和不可预测性占据主导地位。该项目将开发新的算法，这些算法运行在这些自主系统的“大脑”内，使它们能够实现最佳决策，从而提高它们在存在不确定性和有限信息的情况下的可靠性、可预测性、性能和故障安全操作。自动驾驶车辆、拟人机器人、无人机、制造自动化系统和精密手术器械等都将受益于这项研究的结果。虽然这个项目的动机是机器人导航问题，但它解决了人工智能中一个更根本的问题，因此具有更广泛的适用性。该项目将利用随机化图形表示中的技术和随机最优控制理论中的方法，以新颖的方式将两者结合起来，以减少高维自主机器人系统规划和决策过程中的不确定性和不可预测性。本项目将开展的具体研究活动包括：首先，将使用随机化的图通过避免基于不可伸缩的网格技术来获得环境的有效抽象，并应用研究人员开发的新的不确定性传播技术来有效地解决高维空间中的规划问题。其次，随机系统的最优反馈策略将利用最近的前向/后向随机微分方程理论，并结合分层和随机化方法来更好地探索搜索空间。最后，该项目将利用机器学习(ML)的最新进展，并利用在以前类似问题实例中获得的先前经验，在运行时加快优化搜索。该理论的实验验证将在研究人员的实验室进行，包括研究生和本科生。这项研究的结果将通过期刊和会议出版物向社会传播，并为学生提供暑期实习机会，让他们将工作成果转化为现实生活中的工程问题。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。

In recent years, a true revolution is taking place, in the way intelligent machines and robots operate in new, previously unseen, environments and interact with human operators. While in the past robots were primarily found in industrial settings, nowadays autonomous and semi-autonomous robots and systems can be found almost everywhere. This new generation of intelligent autonomous systems will interact even more closely with humans and will help them in their daily lives whether this is work, leisure, and by taking care of many mundane domestic tasks. But world is a messy place. There is a huge difference between a robot operating inside an enclosed “cage” on a factory floor that repeats the same task over and over again, and a robot that needs to navigate in an office environment, in a hospital, or on the highway, where uncertainty and unpredictability dominate. This project will develop new algorithms that run inside the “brain” of these autonomous systems to enable them achieve optimal decision-making, thus increasing their reliability, predictability, performance, and fail-safe operation in the presence of uncertainty and under limited information. Self-driving vehicles, anthropomorphic robots, aerial drones, manufacturing automation systems, and precision surgical instruments among others, will all benefit from the results of this research. Although motivated by robot navigation problems, this project addresses a more fundamental problem in artificial intelligence and thus has a much broader applicability. All applications where a “minimum-energy” path is to be found, e.g., crack propagation in structures, protein folding, data retrieval in high-dimensional spaces, etc., will benefit from the results of this project.This project will leverage techniques from randomized graph representations and methodologies from stochastic optimal control theory, and will combine the two in novel ways, in order to mitigate uncertainty and unpredictability during planning and decision-making for high-dimensional autonomous robotic systems. The specific research activities to be undertaken in this project are: First, randomized graphs will be used to obtain efficient abstractions of the environment by avoiding non-scalable grid-based techniques, along with the application of new uncertainty propagation techniques developed by the investigator to solve efficiently planning problems in high-dimensional spaces. Second, optimal feedback strategies for stochastic systems will be developed by utilizing the recent theory of forward/backward stochastic differential equations, along with the incorporation of hierarchical and randomized approaches to better explore the search space. Finally, this project will take advantage of recent advances from Machine Learning (ML) and the use of prior experience gained during previous similar instances of the problem to expedite optimal search during runtime. The experimental validation of the theory will take place in the investigator’s lab and will involve both graduate and undergraduate students. The results of this research will be disseminated to the community by journal and conference publications and by securing summer internship opportunities for the students to transition the results of their work to real-life engineering problems.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.