CPS: Small: Cyber-Physical Communication for Cooperative Human-Robot Mobility

从事运输和数据收集的人-机器人团队通常会共享一个共同的物理工作空间。该项目将研究人-计算机物理系统(h-CPS)在协作空中有效载荷运输方面的基本挑战。首先，无人机(UAV)在不确定的风向下，在杂乱的环境中协同抬起和携带有效载荷。多无人机系统(MU)自主运行，允许人类同伴在与MU交互时将注意力集中在他们的环境上。我们提出了一种新颖的界面，操作员推着悬挂的有效载荷来引导团队，并通过联网的平板电脑协调任务。一种新的协作控制策略安全地引导MU，而基于物理的算法区分人类输入和环境干扰。飞行测试将演示和验证h-CPS。PI和指导博士后研究人员将让来自代表不足群体的学生和K-12学生参与安全MU飞行演示。该项目提供了三个研究进展：MU可伸缩性和通过连续变形协作控制保证碰撞，安全MU对车辆异常进行补偿，以及易于认知的用户界面。对这项工作特别新颖的是h-cps接口，其中操作员推入有效载荷以引导MUS团队。我们将应用线性动量分析来感知触觉提示，并将在模拟和飞行测试中验证我们的模型。任务级决策将通过将系统建模为马尔可夫博弈来执行，其中博弈状态从人、环境和集合MU状态中分离出来。我们的方法抽象了MU的行为，以降低认知复杂性和实时网络和计算开销。

Human-robot teams engaged in transportation and data collection will often share a common physical workspace. This project will investigate fundamental challenges in human-cyberphysical-systems (h-CPS) for cooperative aerial payload transport. First, Unmanned Aerial Vehicles (UAVs) cooperatively lift and carry a payload through a cluttered environment under uncertain winds. The multi-UAV system (MUS) functions autonomously to allow human companions to focus attention on their environment while interacting with the MUS. We propose a novel interface where an operator pushes on the slung payload to guide the team and coordinates the mission through a networked tablet. A novel cooperative control strategy safely guides the MUS while physics-based algorithms distinguish human inputs from environmental disturbances. Flight tests will demonstrate and validate the h-CPS. The PI and mentored postdoctoral researcher will involve students from under-represented groups and K-12 students in safe MUS flight demonstrations.This project offers three research advances: MUS scalability and collision avoidance guarantees through continuum deformation cooperative control, safe MUS compensation for vehicle anomalies, and cognitively-tractable user interfaces. Particularly novel to this work is the h-CPS interface in which an operator pushes on the payload to guide the MUS team. We will apply linear momentum analysis to sense haptic cues and will validate our models in simulation and flight testing. Mission-level decision-making will be performed through system modeling as a Markov game in which game states are defined from human, environment, and aggregate MUS state. Our method abstracts MUS behaviors to reduce cognitive complexity and real-time network and computational overhead.