Abstract
With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multidimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV.
Abstract (translated)
随着边缘计算技术的出现,远程计算应用的范围已经广泛地拓展了多维度。无人驾驶飞行器任务是利用远程计算促进其性能的重要应用。然而,将计算复杂性转移到远程系统会增加系统的延迟。尽管5G网络技术可以减少通信延迟,但延迟对无人机的控制效果是不可避免的,并且可能不稳定地影响系统。因此,必须考虑系统中的延迟,并在控制设计中对其进行补偿。因此,我们提出了一种新的基于边缘计算的 predictive 控制架构,称为 PACED-5G(使用边缘计算的无人机预测控制)。在所提出的控制架构中,我们设计了一种状态估计器,用于估计当前状态,并通过时间 varying 延迟的可用知识设计了一个模型预测控制器(MPC),用于跟踪参考轨迹,同时避免障碍物,并提供了卸载高级任务于边缘系统的接口。该架构在利用四旋翼无人机的两个实验测试案例进行了验证。
URL
https://arxiv.org/abs/2301.13097
