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新的、以前看不见的环境中运行，并与人类操作员互动。虽然过去机器人主要出现在工业环境中，但如今自主和半自主机器人和系统几乎随处可见。新一代的智能自主系统将与人类进行更密切的互动，并将帮助他们在日常生活中，无论是工作，休闲还是处理许多日常家务。但世界是一个混乱的地方。一个机器人在工厂车间的封闭“笼子”里一遍又一遍地重复相同的任务，而一个机器人需要在办公室环境中导航，在医院里，或者在高速公路上，不确定性和不可预测性占主导地位。该项目将开发在这些自主系统的“大脑”内部运行的新算法，使它们能够实现最佳决策，从而提高其可靠性，可预测性，性能和在不确定性和有限信息下的故障安全操作。自动驾驶汽车、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成果。虽然受到机器人导航问题的启发，但该项目解决了人工智能中更基本的问题，因此具有更广泛的适用性。所有需要找到“最小能量”路径的应用，例如，结构中的裂纹扩展、蛋白质折叠、高维空间中的数据检索等，将受益于该项目的成果。该项目将利用随机图表示技术和随机最优控制理论方法，并将以新颖的方式将两者联合收割机结合起来，以减轻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。在这个项目中进行的具体研究活动是：首先，随机图将被用来获得有效的抽象环境，通过避免不可扩展的网格为基础的技术，沿着与新的不确定性传播技术的应用开发的研究人员，以有效地解决规划问题，在高维空间。第二，随机系统的最佳反馈策略将开发利用最近的前向/后向随机微分方程理论，沿着的分层和随机的方法，以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，并利用以前类似问题实例中获得的经验，加快运行时的最佳搜索。该理论的实验验证将在研究人员的实验室进行，并将涉及研究生和本科生。这项研究的结果将通过期刊和会议出版物向社区传播，并为学生提供暑期实习机会，将他们的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价值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.