An Edge Architecture Oriented Model Predictive Control Scheme for an Autonomous UAV Mission

TL;DR

This paper explores implementing a Model Predictive Control scheme on an Edge Computing device to enhance UAV trajectory control, balancing computational demands and latency issues for improved efficiency.

Contribution

It demonstrates the feasibility of deploying MPC on Edge devices for UAVs, enabling longer prediction horizons and better control performance compared to traditional local or cloud-based solutions.

Findings

Edge-based MPC reduces latency compared to cloud solutions.

Longer prediction horizons improve UAV control accuracy.

Edge computing enables more complex control algorithms on UAVs.

Abstract

In this article the implementation of a controller and specifically of a Model Predictive Controller (MPC) on an Edge Computing device, for controlling the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC requires more computation power in comparison to other controllers, such as PID or LQR, since it use cost functions, optimization methods and iteratively predicts the output of the system and the control commands for some determined steps in the future (prediction horizon). Thus, the computation power required depends on the prediction horizon, the complexity of the cost functions and the optimization. The more steps determined for the horizon the more efficient the controller can be, but also more computation power is required. Since sometimes robots are not capable of managing all the computing process locally, it is important to offload some of the computing process from the robot to the cloud. But then some disadvantages may occur, such as latency and safety issues. Cloud computing may offer "infinity" computation power but the whole system suffers in latency. A solution to this is the use of Edge Computing, which will reduce time delays since the Edge device is much closer to the source of data. Moreover, by using the Edge we can offload the demanding controller from the UAV and set a longer prediction horizon and try to get a more efficient controller.