In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.

中文翻译：

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通过远程非线性模型预测控制实现具有嵌入式防撞功能的自主空中导航的边缘架构

在本文中，我们提出了一种基于边缘的架构，通过启用远程非线性模型预测控制方案来增强资源受限的空中机器人的自主能力，该方案在空中机器人的机载处理器上运行可能需要大量计算。非线性模型预测控制用于控制无人机的轨迹，同时检测和防止潜在的碰撞。所提出的边缘架构能够相对实时地重新计算资源受限的无人机的轨迹，这将使它们具有完全自主的行为。该架构是通过边缘侧的远程 Kubernetes 集群实现的，并在无人机上作为我们的可控机器人进行评估，而机器人操作系统则用于管理源代码和整体通信。通过利用边缘计算和这项工作中提出的架构，我们可以克服资源受限的机器人所具有的计算限制，并提供或改进对于自主任务至关重要的功能。同时，与云相比，我们可以最大限度地减少边缘时间关键任务的相对旅行时间延迟。我们通过利用无人机或边缘资源进行防撞任务的一系列实验来评估系统的行为，从而研究该假设的有效性。