wilamowski-b-m-irwin-j-d-industrial-communication-systems-2011

16-10 Industrial Communication Systems
• Data interoperability: In PROSA, one has to deal with implementation specific data that may
cause difficulties during system installation. In PABADIS, data virtually does not have an established
connection to the ERP system. In PABADIS’PROMISE, the ontology is spread throughout
the entire automation pyramid linking all three layers (ERP–MES–field control) together.
• Control flow: PROSA has a strict vertical control flow that lacks feedback to the upper layer of
the ERP. PABADIS has a limited feedback to the ERP. It actually approaches it only at the end
of the production cycle. PABADIS’PROMISE supports permanent connection with the ERP via
planned, periodic, or event-based reports during the production order life cycle.
16.4.4 Challenges of Industrial Agents’ Usage
All above-mentioned architectures approach the manufacturing automation from the conceptual point
of view and often overlook the application-related aspects that are implied by the distributed nature of
the concepts. In particular, security, product identification, and data interoperability are vital aspects
for practical implementations.
Distributed systems lack a single point of control with decision-making being spread over multiple entities
that communicate with each other. This causes higher security risks, due to the intensive communication
that such architectures require and the fact that there is no single entity that controls the system.
Another challenge for distributed systems is to provide a general overview of the processes and components
on the shop floor. It is difficult to keep track of the products, work-in-progress, and materials in the
plant, which implies that tracking the exact location of pieces is a huge challenge. Therefore, more advanced
identification of the work pieces is required in order to guarantee the efficient operation of the system.
Last but not least, distribution of decision-making functionality requires interoperability of information
flow over all three layers of the automation pyramid as well as mechanisms of distributed databases.
Therefore, a common ontology with mechanisms of data abstraction for different control entities is
required to guarantee the coordination within the system.
16.4.5 Other Application Areas in Brief
While agent-based applications have not yet achieved a noteworthy penetration in industry they nevertheless
are heavily knocking at the door of several industrial application areas, including manufacturing
and supply chain management, logistics, process control, telecommunication, (air) traffic and transportation,
or defense (cf., e.g., [7]). In the center of many of those agent-based industrial systems stand
aspects like coordination, cooperation, self-organization, timely and reasonable/intelligent reactions to
unforeseen developments, planning and decision-making, especially also under real-time constraints
and mainly in distributed areas where the individual units are supposed to be autonomous, and in intelligent
behavior in general.
The aviation and space control industry is another frontrunner of agent technology. Especially,
Âautonomous missions, such as collision control systems for unmanned, often also referred to as “uninhabited”
or “remotely piloted” aerial vehicles (UAV) have attracted a lot of (industrial) research (cf. e.g.,
Â[10–13,15,17,18]). In the AGENTFLY project (cf. [15]) developed in the Gerstner Laboratory, Czech Technical
University (CTU) in Prague has been working with the Air Force Research Laboratory, New York, on
a software prototype supporting the planning and execution of aerial missions of UAVs that includes the
free-flight-based collision avoidance of UAVs with other aerial vehicles. Here, UAVs are truly autonomous
entities whose autonomy is realized by its own set of intelligent software agents. Each flight mission is tentatively
planned before take-off, obviously without being able to consider already likely collisions. During
a flight, the agents detect possible collisions and engage in peer-to-peer negotiations aimed at sophisticated
replanning in order to avoid any collisions. Ref. [10] describes a similar project, in which two different
approaches for adding autonomous control to an existing UAV are explored, tested, and evaluated. The
AERIAL project (cf. [13]) is a distributed system that is able to ensure coordination and control of a fleet
© 2011 by Taylor and Francis Group, LLC