Objectives:

CoBotAGVs allow for creation of a new generation of internal logistics systems dedicated for agile manufacturing and thus increase the availability of production resources by minimizing production disturbances resulting from the introduction of new products and technologies. They can replace classical internal transport systems and make the production more flexible including individual adjustment of production path for each product in order to support mas-customization. At the same time, they allow for better production planning and synchronization of individual stations, which makes production more efficient and profitable. CoBotAGVs can also act as mobile storage space that can be used according to LEAN paradigm applied to agile manufacturing. To take the advantage of the above possibilities, it is necessary to integrate CoBotAGVs with production system, otherwise they won’t be more useful than electric trolleys. The scheduling of logistic tasks has to take into account customers’ orders, availability of production devices, materials and staff and should dynamically react to every change or problem in production process. Therefore CoBotAGV has to be linked both production segments including machines, materials and staff and also to Manufacturing Execution Systems responsible for production management including planning, tracking, optimization and other activities like maintenance.

In order to achieve overall objective of the WP2 four specific objectives have been defined:

• Design and implementation of ontology based information model for CoBotAGV dedicated for internal logistics.
• Implementation of robust and efficient Machine to Machine communication.
• Integration between fleet management system and Manufacturing Execution System including scheduling, tracking, technology management and maintenance
• Creation of agent based simulation environment for production planning and optimizing

Research tasks:

T2.1 Information model

The ontology-based information model adopts information about CoBotAGV to the context of its use, including information processed by the control system, technological information used by MES, information used by Business Intelligence and other parts of the system. The task focuses on:

• interoperability between the various machines, technologies and also personalized for production staff requirements. The model will follow standards recommended for Industry 4.0 applications like: OPC UA OpenPLC, AutomationML, B2MML and other,
• unambiguous presentation of data for cooperating machines, systems, production staff, and for artificial intelligence services supporting the production,
• implementation by object oriented communication middleware based on OPC UA (Open Production Connectivity Unified Architecture).

T2.2 Machine to Machine communication

This task focuses on communication layer implemented in accordance with RAMI4.0 (Reference Architecture Model for Industry 4.0) that describes the Industry 4.0-compliant access to the information and functions of a connected assets by other assets. The service-oriented architecture (SOA) will support communication models defined under T2.1. The research works focus on:

• analysis which data will be used, where it will be used and when it will be distributed,
• architectural design of robust and safe communication including application of different network standards (e.g. IEEE 802.11, 6LoWPAN, 802.15.4m, 5G, hybrid communication systems) and different protocol stack perspectives (CoAP, MQTT, OPC UA, AMQP, XMPP, DPWS, UPnP),
• creation of the set of standardized with Industry 4.0 communication services including relevant configuration and diagnostic tools.

T2.3 Integration between fleet management system and MES

This task focuses on integration layer implemented in accordance with RAMI4.0 with focus on technical functionality. Task research works focuses on:

• definition of formal description of integration models and rules, and the persisting data represented by the models,
• consistent integration of different data, and acquiring new knowledge ,
• creation of services with focus on: (i) production scheduling by detailed planning of internal logistic, (ii) on-line tracking of each logistics task, (iii) reaction to production errors.,
• the communication with MES performed under B2MML language and compatible with ISA95 standard.

T2.4 Simulation environment for production planning and optimizing

Data collected during production activities will be the subject for data mining predictive analysis.  The task focuses on:

• predictions obtained for key parameters according to data model defined in T2.1,
• verifications for the internal logistics plan through simulation,
• the simulations will be performed by agent-based environment that will combine real data that will very detail describe current state of production system with predicted parameters that will be the output for simulation modules,
• simulation results will be compared with actual results and feedback will be send to analytics part.

Research progress: