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。该项目将研究人类-网络物理系统（h-CPS）合作空中有效载荷运输的基本挑战。首先，无人驾驶飞行器（uav）在不确定的风下协同提升和携带有效载荷通过混乱的环境。多无人机系统（MUS）自主运行，允许人类同伴在与MUS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，操作员通过推动悬挂的有效载荷来指导团队并通过联网的平板电脑协调任务。一种新颖的协同控制策略安全地引导MUS，而基于物理的算法将人类输入与环境干扰区分开来。飞行测试将演示和验证h-CPS。PI和受指导的博士后研究员将涉及来自代表性不足群体的学生和K-12学生进行安全的MUS飞行演示。该项目提供了三个研究进展：通过连续变形协同控制的MUS可扩展性和避免碰撞保证，车辆异常的安全MUS补偿，以及认知可处理的用户界面。在这项工作中，特别新颖的是h-CPS接口，操作人员可以通过推动有效载荷来指导MUS团队。我们将应用线性动量分析来感知触觉线索，并将在仿真和飞行测试中验证我们的模型。任务级决策将通过系统建模作为一个马尔可夫游戏来执行，其中游戏状态是de_amp；#64257；需要从人、环境和总体的MUS状态。我们的方法抽象了MUS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.