A motion control system is responsible for controlling moving and/or rotating parts of machines in a well-defined manner, for example in printing machines, machine tools or packaging machines.
A schematic representation of a motion control system is depicted in Figure A.2.2.1-1
. A motion controller periodically sends desired set points to one or several actuators (e.g., a linear actuator or a drive) which thereupon perform a corresponding action on one or several processes (in this case usually a movement or rotation of a certain component). At the same time, sensors determine the current state of the process(es), e.g. the current position and/or rotation of one or multiple components, and send the actual values back to the motion controller. This is done in a strictly cyclic and deterministic manner, such that during one application cycle the motion controller sends updated set points to all actuators, and all sensors send their actual values back to the motion controller. Nowadays, typically Industrial Ethernet technologies are used for motion control systems.
Characteristic parameters and influence quantities for a communication service supporting the cyclic interaction described above.
Control-to-control communication, i.e., the communication between different industrial controllers is already used today for different use cases, such as:
large machines (e.g., newspaper printing machines), where several controls are used to cluster machine functions, which need to communicate with each other; these controls typically need to be synchronised and exchange real-time data;
individual machines that are used for fulfilling a common task (e.g., machines in an assembly line) often need to communicate, for example for controlling and coordinating the handover of work pieces from one machine to another.
Typically, a control-to-control network has no fixed configuration of certain controls that need to be present. The control nodes present in the network often vary with the status of machines and the manufacturing plant. Therefore, hot-plugging support for different control nodes is important and often used.
Control-to-control communication between different motion (control) subsystems, as addressed in subclause A.2.2.1
. An exemplary application for this is large printing machines, where it is not possible or desired to control all actuators and sensors by one motion controller only.
Control-to-control communication between different motion (control) subsystems. Exemplary application for this are extra-large machines or individual machines used for fulfilling a common task (e.g., machines in an assembly line).
Mobile robots and mobile platforms, such as automated guided vehicles, have numerous applications in industrial and intra-logistics environments and will play an increasingly important role in the Factory of the Future. A mobile robot essentially is a programmable machine able to execute multiple operations, following programmed paths to fulfil a large variety of tasks. This means, a mobile robot can perform activities like assistance in work steps, collaboration with other robots, e.g. for car assembly, and transport of goods, materials and other objects. Mobile robot systems are characterised by a maximum flexibility in mobility relative to the environment, with a certain level of autonomy and perception ability, i.e., they can sense and react with their environment.
Autonomous guided vehicles (AGVs) are a sub-group of mobile robots. AGVs are driverless and used for moving materials efficiently within a facility. A detailed overview of the state of the art of autonomous-guided-vehicle systems is provided elsewhere in the literature 
. All mobile robots incorporate all functionalities needed for an AGV.
Today, the AGV's control intelligence is hosted inside the AGV. In the future, centralised fleet control will be hosted in the edge cloud, which will require reliable wireless communication between the control entity and all AGVs. Also, the current paradigm of pre-describing a route for the AGV will be replaced with target-based navigation. This paradigm change will make AGV routes more flexible.
Mobile robots are monitored and controlled from a guidance control system. Radio-controlled guidance control is necessary to get up-to-date process information, to avoid collisions between mobile robots, to assign driving jobs to the mobile robots, and to manage the traffic of mobile robots. The mobile robots are track-guided by the infrastructure with markers or wires in the floor or guided by own surround sensors, like cameras and laser scanners.
Mobile robot systems can be divided in operation in indoor, outdoor and both indoor and outdoor areas. These environmental conditions have an impact on the requirements of the communication system, e.g., the handover process, to guarantee the required cycle times.
Where this document does not explicitly refer to AGVs, the term mobile robots
applies to AGVs as well as to mobile robots.
Periodic communication for the support of precise cooperative robotic motion control (transfer interval: 1 ms), machine control (transfer interval: 1 ms to 10 ms), co-operative driving (10 ms to 50 ms).
Periodic communication for video-operated remote control.
Periodic communication for standard mobile robot operation and traffic management.
Real-time streaming data transmission (video data) from a mobile robot to the guidance control system.
AGVs have the following needs.
The direct-device control is time-critical since the communication involves safety-relevant functions such as emergency stop and the avoidance of obstacles.
For the implementation of swarm intelligence, position and availability information are needed. A possible route change due to a blocked route affects the routes of all other AGVs that will follow. The communication is less time-critical than for safety-relevant functions.
Camera-based navigation requires high data rates. Examples for camera-based navigation are the positioning of boxes/containers, detection of persons and obstacles, as well as creation and administration of a map for flexible navigation. Note that sensor-based navigation requires lower data rates than camera-based navigation.
AGV route control and emergency safety related time critical latency and response can be achieved with an edge cloud where the edge infrastructure is located close to the AGVs.
Mobile robots have additional needs.
The mobile robot provides an additional service during transport (for instance quality inspection, scanning of surroundings, asset identification, carrying of work pieces). In order to reduce the uplink data rate, pre-compression of data is possible directly on the device. The communication is not or at least less time-critical than the motion control of the mobile robot.
The mobile robot needs to interact with the periphery (for instance intelligent storage racks, stationary robots, and moving machines). This communication is time-critical. Interaction with the periphery can be relevant at the start point, end point, and also at several intermediate stations (for instance the collection of parts from intelligent storage racks). The exact position and orientation can be determined by a centering station and the AGV sensors. Additional scanning by the robot with a video camera may be necessary.
For some mobile robots, their control intelligence might be centralized and hosted in an edge cloud. They require secure communication towards the edge cloud. If the path layout such a mobile robot follows (e.g., including indoor and outdoor) causes it to switch the communication between a private network and a public network, consideration should be given to provide the required level of security for communication.
Mobile robots whose control intelligence is centralized and hosted in the edge cloud needs privacy protection of data stored in the edge cloud.
In order to be able to process time-sensitive data from local sensors and devices (AGVs, robots etc.) in an edge cloud, the edge infrastructure needs to be on-premise or close to the factory.