Towards Low Latency Industrial Robot Control in Programmable Data Planes

Abstract

Due to the advanced control and machine learning techniques, today's industrial robots are faster and more accurate than human workers in well-structured repetitive tasks. However, in case of sudden changes in the operational area, such as unexpected obstacles or humans, robots have to be continuously monitored by powerful controllers for swift interventions (i.e., send emergency stop signals). As in the case of many verticals (e.g., transportation, shopping), the proliferation of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has started to captivate industry 4.0 as well in order to benefit from low infrastructure costs, and flexible management and resource provisioning. Besides all the advantages of the centralized approach, however, in critical situations (e.g., possible collisions, actuator damages or human injuries) the required ultra-low latency between the robots and the controller becomes an all-important factor, and one of the main concerns, at the same time, for industry leaders making the decision towards this paradigm shift. In this paper, we argue that by relying on recently emerged stateful and programmable data planes, it is possible to fill this gap by offloading latency-critical applications to the network, thereby bringing some intelligence much closer the robots. We present the first in-network robotic control application that is capable to intercept the communication between the robot and the controller and craft responses immediately if needed. In particular, we show that we can detect position threshold violations entirely in the data plane, close to the robot, and deliver emergency stop commands within no time with full compliance to the actual TCP session and application states.