Towards a Multi-Agent Platform for Cyber-physical Systems

based on Low-power Microcontroller for Automated Intralogistics

A Minimized Embedded Solution for the Internet of Things

in Intralogistical Environments

Arne Stasch

and Axel Hahn

OFFIS Institute for Information Technology, Oldenburg, Germany

Department of Computing Science, Carl von Ossietzky University, Oldenburg, Germany

Keywords: Cyber-physical System, Multi-Agent System, Material Flow, Logistics.

Abstract: Today the fluctuating market requires more flexibility from central controlled material flow systems. A new

approach such as the Internet of Things is able to turn the common structure into a cognitive decentralized

system. This paper addresses a modularized solution for material flow systems in intralogistical

environment. The promising concept of Internet of Things leads to an idea of a cyber-physical system with a

Multi-agent platform which is presented in this paper. In order to develop a system which is capable of

meeting industrial needs, a low-power microcontroller is chosen as basis for the Multi-agent system. The

combination has advantages but also restrictions which are discussed. A demonstrator for the future

implementation of the system and evaluation is introduced.

1 INTRODUCTION

Flexibility is an increasing key requirement in

intralogistical environments. The material flow

systems (MFS) used in this field have to bear

alternations in the flow rate and flow directions.

Common material flow systems consist of

centralized programmable logic controllers (PLC)

and are designed for a certain capacity. By the time

requirements of the MFS change so that a

retrofitting is necessary or reasonable, then a conflict

occurs: Changes of the MFS are time- and cost-

intensive for logistic providers.

Modular material flow systems are one solution

of this problem (ten Hompel, 2011). Among the

approaches to transfer promising technologies to a

modular MFS, the concept of “Internet of Things”

sticks out (Günthner, 2010). In this concept, the

intelligence is distributed over a decentralized

control system. This results in engineering

advantages. The reusability of soft- and hardware

solutions will increase and the systems diversity will

be reduced for example (ten Hompel, 2011). This

effect will reduce development and working costs.

The MFS control architecture was basically

stacked in three layers so far (Günthner, 2010).

These layers are the physical, the PLC and the

coordination layer (see Figure 1). The physical layer

represents mechanical and electrical parts. These are

controlled by the PLC layer which accesses sensors

and actuators. The other layers are coordinated by

the coordination layer which consists of an IT-

infrastructure.

Figure 1: Transferring of common MFS layer into the

concept Internet of Things.

In order to modularize the common MFS, every

module must implement this structure. Figure 1

visualizes the change to a decentralized concept.

429

Stasch A. and Hahn A..

Towards a Multi-Agent Platform for Cyber-physical Systems based on Low-power Microcontroller for Automated Intralogistics - A Minimized Embedded

Solution for the Internet of Things in Intralogistical Environments.

DOI: 10.5220/0004583004290433

In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2013), pages 429-433

ISBN: 978-989-8565-71-6

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

The concept presented in this paper takes this

modularized structure and transfers it into an

embedded solution. Every module has its own

physical sensors and actuators (Physical Layer).

They are controlled by a microcontroller with a

Multi-agent system (MAS) platform (PLC Layer).

Agents coordinate their tasks through a fieldbus

(Coordination Layer).

The “Internet of Things” can be implemented in

different ways such as Near Field Communication

(NFC), Wireless Sensors and Actuators Networks

(WSAN) or IP for Smart Objects (IPSO) (Atzori,

2010). Two appealing ways are described in the

following. Radio Frequency Identification (RFID)

tags allows packages to carry their own information

or code which can be altered. Multi-agent systems

(MAS) are systems which consist of entities which

can collectively solve problems (Wooldridge, 2002).

Those technologies are solutions to distribute the

intelligence.

The RFID tags have to be attached on packages

and need an intelligent infrastructure.

Electromagnetic interference (EMI) and certain

materials (metals, liquids) can restrict the use of it in

industrial environments (White et al., 2007).

A MAS needs a modularized infrastructure to be

meaningful. The task, the MAS has to fulfil, has to

be divisible so that different agents work on the

same task. Further a platform that gives multiple

agents the ability to communicate and interact with

its environment is needed. To approve the reliability

of a system which coordinates through

communication, a pervasive testing is necessary.

Taking the idea of RFID with adaptable

identities (ID) where the packages can represent

themselves in the software and transfer this to an

agent who represents the package in a MAS is on a

par with the idea of ten Hompel (ten Hompel, 2011).

Creating a MAS which can be used in an industrial,

intralogistical environment on a modularized MFS is

the next step.

MAS are used in different applications like

autonomous driving (Beeson, 2008) or decentralized

control of Automatic Guided Vehicles (AGV)

(Weyns, 2008). These approaches use computer

systems to realize the MAS infrastructure. The

approaches of geo-references swarm agents

(Barbera, 2010) or a hardware platform for analogue

power simulator as a test environment for smart

grids (Spencer, 2010) appeal more suited for a

decentralized control system. They use embedded

controllers as basis but one controller represents

only one agent. This is not enough for a platform

that has to handle more than one agent. With the

above mentioned MAS for MFS such an embedded

software platform is needed and is presented in

section 3.2.

Modules which communicate with each other

and interact with the environment physically are

called cyber-physical systems (CPS) (Lee, 2008).

Therefore the aim will be to create a CPS which can

replace common MFS. Microcontrollers can meet all

industrial standards and are widely used. Although

they are not powerful, they meet the requirements

for networked systems which can divide their tasks.

The decision of taking microcontrollers as basis is

discussed in the next section.

The paper is arranged in the following order: The

concept of a new cyber-physical system is presented

in section 2. In section 2.1 MAS frameworks and

their benefits are discussed in the described scenario.

The own idea of a MAS platform is presented in

section 2.2. In the following section 2.3 the

demonstrator which will be used to implement the

developed control system is described and is

followed by the conclusion in section 3.

2 A CONCEPT FOR A NOVEL

CYBER-PHYSICAL SYSTEM

The goal is to create a CPS which can substitute the

common MFS and deliver new features as loose

coupling. It should be possible to couple or decouple

hardware parts and the software completes the

reconfiguration autonomously. If the MFS will be

split in fine-meshed modules then there are more

options to rearrange the MFS to be on a par with the

changing requirements. Every storage shelf,

transport-vehicle and reasonable smallest part of a

conveyor can be a capsuled module. This means that

in every module an electronic control unit (ECU)

has to control the module and communicate with

other modules.

Communication between these modules is the

key for smooth control and coordination. Every

entity has to fulfil the functions of all layers,

mentioned in section 1, but only between the borders

of the module. The modules must communicate to

achieve the tasks of the whole MFS.

By creating a modular system, the system

structure has to be changed (ten Hompel, 2011).

Figure 2 shows the previous horizontal view and the

modularized vertical view of the systems structure.

In every development step the vertical view must be

considered.

These separated modules only need the resources

for a small contingent of the whole system. An

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embedded solution sticks out against PCs and PLCs

because a module does not need the processing

power of these. Small, optimised, real-time capable

systems which can be mass-produced for low cost

could be enough for the task.

Figure 2: Changing horizontal to vertical system view.

Hardware-requirements for an ECU are:

1. Enough processing power to control the

mechatronic parts and handle the

communication to other modules.

2. Low-power consumption for modules which

are not connected to a static power supply.

3. Reliable for safety critical applications

4. Interfaces are compatible to industrial

standards.

These requirements are not very specific.

However they give a direction to small

microcontrollers as MAS devices. After defining the

hardware, suitable software is needed. This is

discussed in the next section.

2.1 MAS Framework for embedded

Solutions

A multi-agent system (MAS) delivers handy

functionality for MFS. Agents represent the modules

and packages in the software. This distributes the

responsibility for the package transport to the

modules. Other functionalities like routing can be

implemented as agents, too.

Requirements, like real-time operations,

industrial standards and small footprints sort out

most of the existing MAS frameworks. JADE

(Bellifemine, 1999) is a very common used MAS

framework but doesn’t meet the requirements stated

above. Mobile C uses C/C++ as programming

language and C meets industrial standards (Chou et

al, 2010). However Mobile C needs a General

Purpose Operating System (GPOS) and even the

smallest embedded versions, e.g. Embedded Linux,

need more footprint than 259 KB Flash Memory

and this does not account RAM usage.

http://www.lynuxworks.com/products/whitepapers/xp-vs-

linux.php3: conclusion last visited 02.04.2013

In the search of agent friendly architectures

AUTOSAR (AUTomotive Open System

ARchitecture) stands out (Heinecke, 2004). It is a

standardized system which solves software

development issues for automotive ECUs. The

architecture is layered and separated between

applications and basic software which is dependable

on hardware. The basic idea is to reuse written

applications on any device independent from

hardware. This architecture allows the applications

to communicate to other applications on the same

ECU and on other ECUs with the same interface.

The interface is provided by the AUTOSAR runtime

environment (RTE). A patent from the DAIMLER

describes the use of agents with AUTOSAR for

ECU Diagnostics.

Because of the specifications from the

automotive sector, AUTOSAR is not lean. It has a

footprint of 256KB on the leanest AUTOSAR ECU

(Bunzel, 2011). It is not optimised for agents and

their interactions. Exemplary, only one application

has access rights to a specific part of the memory

supplied by the RTE. This application has to manage

and deliver it again to other applications with ports

through the RTE. Therefore the implementation of

the agent communication which needs access to

memory is more complicated.

The listed memory resources do not look much

in the embedded world today. This approach is to

aim for a minimal, reliable and cheap solution for

industrial use. The MAS framework should be lean

as possible because the embedded environment

should be chosen due to the application and not on

the framework. A suitable MAS platform is

presented in the next section.

2.2 Concept of MAS Platform for CPS

Using the software architecture of AUTOSAR gives

a basis for a MAS framework which suits for this

CPS. Figure 3 depicts an adapted AUTOSAR

architecture. On the ground level, the ECU hardware

is represented. The background level (in AUTOSAR

language basic software) represents the Real-Time

Operating System (RTOS), the hardware drivers,

and the driver’s service interfaces which are unified

APIs. Agents reside in the Agent Level were an

Agent RTE connects them with each other and the

Background Level.

Creating the Agent RTE will be the key task in

the future work. It has to support the agents with

Patent Daimler AG: (WO2008095518) USE OF A

DISTRIBUTED DIAGNOSTIC ARCHITECTURE IN

AUTOSAR

TowardsaMulti-AgentPlatformforCyber-physicalSystemsbasedonLow-powerMicrocontrollerforAutomated

Intralogistics-AMinimizedEmbeddedSolutionfortheInternetofThingsinIntralogisticalEnvironments

431

Figure 3: Adapted AUTOSAR Architecture.

all necessary information and access to fulfil their

role autonomously. Every Agent needs to run in a

capsuled task so it can act independently. The Agent

RTE has to manage all communication between the

agents and agents on other modules. In Figure 4 the

Routing Agent communicates with a Package

Agent on the same module and with a Routing

Agent on another module. The Agent RTE and the

services below handle the communication. However,

the interface the agents use is independent from their

location.

Figure 4: Agent-to-Agent Communication.

The functionality the Agent RTE has to provide

the agents has to be altered by the knowledge about

the functionality of other MAS frameworks like

Mobile C. Because the limitations of the

microcontroller can hinder the development of some

features, it has to be analysed whether it is a fully-

fledged MAS platform or it has reduced

functionality.

In the next section the environment where this

MAS framework will be implemented is introduced.

2.3 Implementation in a Material Flow

System

The research project CogniLog (Overmeyer, 2012)

deals with automated, cognitive logistic-networks. A

demonstrator composed of conveyor modules and

forklifts will be built. It will show the capabilities of

modularized conveyor systems.

The setup consists of conveyers, Industrial PCs

(IPC) with Soft PLCs, frequency convertors,

electrical engines, and sensor barriers. A Profibus

fieldbus is responsible for the communication

between the electronic parts.

Per every “capsuled platform” a microcontroller

will be implemented. A “capsuled platform” is

defined as a conveyor which actuators control solely

itself and no other platforms. The intersections must

have sensor barriers to detect incoming or outgoing

goods. The microcontroller completes the capsuled

platform to a module.

In a full modularized environment the

microcontrollers replace the PLCs. To minimize

development time the PLCs remain as a middleware

for actuators, sensors and bus communication. The

microcontrollers represent the distributed

intelligence. They will do the planning,

coordination, and monitoring.

The microcontrollers which are used are Texas

Instruments (TI) MSP430F6638 with a 16-Bit

Architecture, 256 KB flash memory, 16 KB RAM,

SPI and I²C Interfaces. An EEPROM of 128 KB

non-volatile memory and a Proficonn

Profibusadapter is connected with the SPI Interface.

In figure 5, a draft of the connections between

the different electronic parts is shown. The modules

represent the separated conveyor parts.

Although this setup does not correspond with the

targeted decentralization, it shows most of the

functionality which a fully decentralized system will

have.

The previously described demonstrator setup

uses only established technologies. The MFS can be

logical modularized but not real physical. The

conveyor parts are heavy and not exchangeable.

Heavy electrical cabinets and many electrical cables

do not allow fast reconstruction of the whole setup.

But a conveyor system which can be rearranged is

not the aim of this research. The purpose is to create

a technology that enables the creation of such a

system. In this setup the MAS platform can be tested

on the usability of coordination on communication.

The loose coupling can be shown by connecting and

disconnecting modules to simulate a retrofitting.

Additionally the system has to fulfil the same role as

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a common MFS setup. No information should be

lost in the communication and no package routing

problems should occur.

Figure 5: CogniLog demonstrator setup.

This should demonstrate that small embedded

controllers with a proper multi-agent system

framework can be a flexible way to bring the ideas

of “Internet of Things” to material flow systems.

3 CONCLUSIONS

This paper describes the ideas of a modularized

intralogistical control system. After a summary about

suitable technologies a new approach is proposed.

Capsuled modules, which consist of their own sensor

barriers, actuators, intelligence and communication

abilities, form a new MFS. The intelligence is

distributed through a MAS platform for small

embedded controllers. This platform will be

constructed for this purpose. This paper also describes

the implementation environment inside the CogniLog

project. This will be an authentic test environment of

the usefulness of this approach of a CPS.

After the implementation phase the Agent RTE

has to be compared with other MAS frameworks to

evaluate the different features and cuts. When this is

done, the possible features can forecast a profit in

other domains. If a mechatronic task can be split in

many parts which do not need many processing

power, the new MAS platform could bring an

advantage.

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TowardsaMulti-AgentPlatformforCyber-physicalSystemsbasedonLow-powerMicrocontrollerforAutomated

Intralogistics-AMinimizedEmbeddedSolutionfortheInternetofThingsinIntralogisticalEnvironments

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