NRI: Planning, Collaborative Guidance and Navigation in Uncertain Dynamic Environments

动态、不确定环境中的导航是一个困难而又普遍存在的问题，广泛应用于搜索和救援、协调运动、分布式监控和监视等领域。 协作解决方案非常有前途，因为它们有潜力利用人类和自动化的优势。 然而，协作导航的主要挑战不仅包括底层自动化有效处理设计阶段可能未考虑的新场景和不断变化的环境的能力，还包括人机交互需求。 能够解决这些挑战的方法和工具的设计可以在人机协作系统中实现全新的功能。 所提出的研究的新奇是在控制理论，运动规划和人类指导的集成，提供高效的解决方案，在高度动态和不确定的环境中导航。 拟议的项目还将创造机会，让代表性不足的少数民族参与K-12外联以及本科生和研究生研究，促进跨学科合作，并开发新的跨学科研究生课程。该提案旨在开发一个通用的框架，在复杂的环境中，可以容纳数百个移动的障碍物（可能随机动态），非平凡的静态障碍物，和人类在循环中的协同导航。 我们建议a）评估高度动态环境中用户和自治系统的短期和长期信息之间的权衡，B）将我们现有的算法技术扩展到更高复杂度的环境，例如，多机器人和非平面环境，c）设计和测试几个用户界面，满足用户可观察性和用户可预测性的预定条件，以提高安全导航的有效性，以及d）通过Android应用程序实验验证我们现有的动态，不确定环境中的协同导航设置。我们将开发紧密耦合的规划和控制工具，将人工指导和决策与自动化工具相结合，并在高度动态的不确定环境中完成严格的安全分析。 所开发的方法将在多种环境中进行验证，与人类受试者，并在微型机器人试验台。

Navigation in dynamic, uncertain environments is a difficult yet ubiquitous problem in diverse applications such as search and rescue, coordinated movement, distributed monitoring and surveillance. Collaborative solutions are highly promising because of their potential to exploit strengths of both the human and the automation. However, major challenges in collaborative navigation include not only the ability of the underlying automation to effectively handle novel scenarios and changing environments that may not have been considered at the design stage, but also human-automation interaction requirements. The design of methods and tools that can address these challenges could enable fundamentally new functionality in collaborative human-robot systems. The novelty of the proposed research is in the integration of control theory, motion planning, and human guidance to provide highly effective solutions for navigation in highly dynamic and uncertain environments. The proposed project will also create opportunities to involve under-represented minorities in K-12 outreach and in undergraduate and graduate research, to facilitate interdisciplinary collaboration, and to develop a new interdisciplinary graduate course. This proposal aims to develop a generic framework for collaborative navigation in complex environments that can accommodate hundreds of moving obstacles (with possibly stochastic dynamics), non-trivial static obstacles, and humans in the loop. We propose to a) evaluate the tradeoff between short-term and long-term information for both users and autonomous systems in highly dynamic environments, b) extend our existing algorithmic techniques to environments of higher complexity, e.g., multi-robots and non-planar environments, c) design and test several user-interfaces, which satisfy pre-determined conditions for user-observability and user-predictability, for their effectiveness in improving safe navigation, and d) experimentally validate our existing setup for collaborative navigation in dynamic, uncertain environments via an Android app. We will develop tightly coupled planning and control tools, integrate human guidance and decision making with automated tools, and complete a rigorous analysis of safety in highly dynamic environments with uncertainty. The developed methods will be validated in multiple environments, with human subjects, and on a micro robot testbed.