Two-tier MPC architecture for AGVs navigation assisted by edge computing in an industrial scenario

Abstract

The next generation of mobile networks may be a bridge to move industrial control to edge computing. Embedded controllers physically installed in the industrial process can be replaced by software remotely executed as a service. In this context, Automated-Guided Vehicles (AGVs) are industrial agents that can benefit from this scenario by offloading computing-intensive tasks remotely to the edge. Additionally, an AGV control system based on remote software promotes flexibility, an essential requirement for Industry 4.0. Considering the remote control of AGV, the mobile robot may traverse areas where the signal is degraded, increasing the risks of collisions and accidents due to connection failures. Previous works suggest the adoption of Model Predictive Control (MPC) to control mobile robots in the occurrence of delays and packet losses. In this article, we propose a two-tier architecture of MPCs, one executed at the edge to plan the trajectory of multiple AGVs globally and the other executed individually in each AGV to keep it on the planned track. In simulations performed in an edge computing environment using a robot simulator, the vehicles follow the planned trajectory even with network degradation. In our simulated scenarios with delays and packet loss, predicted actions planned by the MPC on the edge avoided collisions between the vehicles.