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Sistemas Multi-agentesno contexto da Indústria 4.0

Paulo Leitãopleitao@ipb.pt

http://www.ipb.pt/~pleitao

Seminário de Sistemas Inteligentes, Interacção e Multimedia

Porto, 9 de novembro de 2017

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Agenda

Indústria 4.0

Sistemas multi-agente (MAS) como uma solução para desenvolver sistemas ciber-físicos (CPS)

Discussão de aplicações de MAS na indústria e em projetos R&D

Análise das barreiras para uma maior adoção de MAS pela indústria e desafios futuros

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Agenda

Indústria 4.0

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Evolution of complexity

Spirit of St. Louis,National Air and Space Museum, Smithsonian Institution

Airbus A380

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Complexity in engineering problems

Processes plants

Logistics

Smart grids

Taxis fleet

Manufacturing plants

Airport management

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New demands in manufacturing

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Markets are imposing strong changing conditions

Customization of products in high flexible production

Reduction of time to reconfigure

(usually weeks and months)

Plug and produce

Time on market / Time to market

Tesla’s robotic factory in Fremont, California

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Moving to decentralized structures

Traditionally: centralized and monolithic structures

Production processes

Sensors / actuators

Control

SCADA

MES

ERPANS/ISA

95

Challenge: decentralize and distribute functions

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Industrie 4.0

• Digitization of traditional factories, aiming:

– Modernization of industry (intelligent factories)

– More productive, efficient, flexible, adaptive reliable

globally more competitive!

• Based on:

– CPS systems

– Using emergent technologies, particularly IoT and IoS

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Key Design Principles

• Decentralization

• Connectivity and interoperability

• Service orientation

• Optimized and real-time decision-making

• Modularity

• Virtualization (modelling and simulation)

• Human-machine integration

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Disruptive technologies enablingdigitization

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These concepts around the world …

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Fourth industrial revolution

1st: mechanization of production using water

and steam power

2nd: mass production with the help of electric power

3rd: use of electronics and IT to further

automate production

4th: ICT, IoT, CPS, Self-*

1780 - 1830

1900 - 1930

1955 - 1970

2013 -

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Why is seen as an revolution?

• Challenges that force a disruptive technological change!

• Examples:

– Big data analysis

o Huge amount of data to be collected and processed in real-time

– Additive manufacturing

o More complex parts, faster and with more quality

– Collaborative robots

o Already available for small/medium sizes but what happen for robots with higher speed and payload? (example 5 m/s and 1ton)

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Industrial Internet

• GE, Intel, IBM, Cisco, US Government, …

• Integration of complex physical machinery with networked sensors and software

• Broader than industrial production, e.g., also smart electrical grids, transportation

• Several topics to process data from machines

– E.g., machine learning, big data, Internet of things, M2M communication

• Analyze collected data (often in real-time) and dynamically adjust operations

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Waves of innovation and change

Source: Peter C. Evans and Marco Annunziata, «Industrial Internet: Pushing the Boundaries of Minds and Machines”, General Electric, November 26, 2012

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Cyber-physical systems (CPS)

• Concept introduced in 2006

• Featuring:– Tight combination of

computational and physical elements

– Distributed organized in a network

– Interaction each other to achieve a common goal

• Intelligent, dynamic and self-* large-scale systems, covering inter- and intra-enterprise integration

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CPS vs ES and CPS vs IoT

• CPS vs embedded systems (ES)

– ES: computational elements hosted in stand-alone devices

– CPS: network of interacting computational and physical devices

• CPS vs IoT

– IoT : focus is in the interconnection of cooperative objects

– CPS: also considers the computational decisional components to provide intelligence, responsiveness and adaptation

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CPS in practice

Cyber-physical components

DB Intelligent, self-contained and plugable SW

modules

Intelligent, self-contained and plugable HW

modules

global self-adaptation and

self-optimization

self-learning

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Application domains

Smart buildings

Smart electrical grids

Smart transportation and mobility

Smart manufacturingSmart healthcare

Defense

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Challenges

• Way to realize Industrie 4.0 and Industrial Internet

• “CPS are now a national priority for federal R&D”President’s Council of Advisors on Science and

Technology (PCAST) in a 2007 report

• Novelty is not in new technology but in the way to combine existing technologies!

• Heterogeneous HW and SW technologies

• Interconnection with legacy systems and HW devices

• Such systems can be difficult and costly to design, test and maintain

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Agenda

Sistemas multi-agente (MAS) como uma solução para desenvolver sistemas ciber-físicos (CPS)

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MAS for distributed intelligence

• Agents to introduce intelligence and adaptation

• Agents are not a restyling of objects!

inter-agent communication

decision

physical interface

An autonomous component that

represents physical or logical objects,

capable to act in order to achieve its

goals, and able to interact with other

agents to reach its objectives.

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MAS paradigm

• Rare applications consider agents in an isolated manner multi-agent systems!

Network of intelligent agents …,

capable to interact to reach their

individual goals when they haven’t

enough knowledge/skills

• “Intelligent” behavior emerges from the interaction among distributed agents

• Characteristics: modularity, flexibility, robustness, adaptability and re-configurability

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MAS working in practice

visão local

Comportamento local

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What multi-agents can offer

Reusabilityold components can be re-used to develop new components or new systems

Distributed thinkinga complex problem can be divided into several small problems

Modularitybuilding the system by

pieces like using LEGO

Robustnesslosing one decision node doesn’t implies the system failure

Reconfigurabilitychanges can be performed on the fly

Smooth migrationfrom old technologies to new ones

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Need of MAS in practice

• System comprising a sequence of modular conveyors

• Individual conveyor comprises:

- 1 motor

- 2 light sensors

How to implement the control system?

S0

S1

C1 C2

- C1 only stops when the part arrives to S1

- C2 starts when the part arrives to S0

• Transfer a part from an input to an output location

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Using the traditional solution

• Use a centralized logical control approach

• Programmed using IEC 61131-3 running in a PLC

• Advantages:

– Simple to program

– Industrial adopted

• Disadvantages:

– Lack in supporting scalability and re-configuration of the conveyor system

– Interdependencies between conveyors increases development effort and time!

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Particularly, what happen if we need to …

• Switch the order of the conveyors?

We need an alternative design approach to support the easy reconfiguration on-the-fly!!!

A B C DBC

• Add a new conveyor?

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Using the MAS solution

• Create cyber-physical components

Source: J. Barbosa, P. Leitão, J. Teixeira (2017), “Empowering a Cyber-Physical System for a Modular Conveyor System with Self-organization”, Proceedings 7th Workshop on Service Orientation in Holonic and Multi-agent Manufacturing (SOHOMA'17).

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Intelligent MAS solution

Each agent has a modular and similar logical control

Instantiate as many agents as modular conveyors

Agents interact among them to get sequence knowledge

Plugability and reconfiguration on-the-fly!

video

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Combining with IEC 61131-3

• MAS usually misses real-time constraints

• Preserve low-level control to ensure responsiveness

How to standardize the

interface?

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IEEE P2660.1 Working Group

• Recommended practices to solve the interface problem

– integrating software agents with low-level real-time automation control devices

– in the context of cyber-physical systems

– allowing the reuse and transparency

• Contributions are welcome!

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Integration of agents and SOA

• SOA have potential to solve interoperability problems

• New design approach:

Agents: implement control functions

Services: encapsulate functionalities provided by agents

• Similarity with the LEGO concept

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Agents distributed among different levels

Cloud level to make accurate and

optimized analysis

Edge and fog levels to make a fast (real-

time) analysis

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The role of edge vs cloud

• Processing as close to source as possible

• Real-time monitoring and decision-making

• Fast response to condition change

• Data pre-processing and filtering

Cloud layer Edge layer

• Central data aggregation

• Long-term data processing

• Historical analysis of data

• Optimization and prediction

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Agenda

Discussão de aplicações de MAS na indústria e em projetos R&D

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MAS applications in industrial domain

1995 20152005 20102000

EXPLANTECH

FABMAS

CambridgePacking Cell

ABAS

AgentSteel

Air cateringscheduling

NovaFlex

SemanticMAST

ADACORFMS AgentFly

ChilledWaterSystem

Axion-Holding scheduling

Ciudad Real

Airport

Source: P. Leitão, V. Marik, P. Vrba (2013), “Past, Present, and Future of Industrial Agent Applications”, IEEE Transactions on Industrial Informatics, vol. 9, n. 4, pp. 2360-2372 (DOI: 10.1109/TII.2012.2222034)..

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Objectives

Partners:

• Integration of quality control and process control in real-time

• Use of a distributed MAS infrastructure to support feedback control loops

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• Preserving existing low-level control

• Distributed collection of data in real-time

• Correlation of data in real-time using data analysis

Functionalities

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• MAS solution developed using JADE

• Installed in the Whirlpool’s factory plant producing washing machines

• 11 QCAs and 6 RAs were running in PCs distributed along the production line

• In stable production flow, approximately 400 PAs are running simultaneously

Deployment numbers

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Examples of intelligence” for data analysis

grace-demo

• Early detection of non-conformities

Customization of functional tests according to the on-line gathered production data

Customization of the controller’s parameters of each machine considering the production data

Early detection of products that never reach desired quality

Dynamic adaptation of process parameters considering data gathered from quality control

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Main benefits

• Increase of production efficiency

• Reduction of non-conformities

• Increase of products’ quality

• Reduction of costs related to scraps

• Increase of products’ customization

Source: P. Leitão, N. Rodrigues, C. Turrin, A. Pagani (2015), “Multi-agent System Integrating Process and Quality Control in a Factory Producing Laundry Washing Machines”, IEEE Transactions on Industrial Informatics, vol. 11, n. 4, pp. 879 - 886 (DOI: 10.1109/TII.2015.2431232).

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• Mitigation strategies to respond faster to unexpected events in ramp-up production of complex and highly customized products

• ESB integrating heterogeneous SOA and knowledge-based MAS applications

Objectives

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• Integration of strategic planning and operational scheduling systems

• Real-time data analysis, namely to detect deviations, tendencies, etc.

• “What-if” simulation to support strategic decisions

Functionalities

Source: P. Leitão, N. Rodrigues and J. Barbosa (2015), “What-if Game Simulation in Agent-based Strategic Production Planners”, Proceedings of the 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'15), Luxembourg, 8-11 September, pp. 1-8 (DOI: 10.1109/ETFA.2015.7301438).

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Strategic Planner

Operational Scheduler

Scenario Designer

Manufacturing Incident Detection

Agent Solution

iESB (Intelligent Enterprise Service Bus)

Life-cycle Management

Data Transformation

Node Management

Sniffer

ARUM database

(tripe store)

Ontology service

Legacy data

sources SC

AD

A

ER

P

ME

S

GatewaysGateways

Gateways

Architecture

Source: C. Marín, L. Mönch, P. Leitão, P. Vrba, D. Kazanskaia, V. Chepegin, L. Liu, N. Mehandjiev (2013), “A Conceptual Architecture Based on Intelligent Services for Manufacturing Support Systems”, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC’13), pp. 4749-4754 (DOI: 10.1109/SMC.2013.808).

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• Main benefits:– Reduction of the ramp-up time

– Early detection of events and failures

– Improvement of the adaptation to the condition changes

– Improvement in strategic decision-making facing unpredictability

arum-demo

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Consortium

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Agenda

Análise das barreiras para uma maior adoção de MAS pela indústria e desafios futuros

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Road-blockers

investment

distributed thinking interoperability

scalability standardization real-time constraints

integration with physical devices supporting

technologies and methodologies

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Current trends

• Convincement of industry stakeholders of the benefits of this approach

• Compliance with industrial standards

• Technical issues (relevant in industrial systems): interoperability, resilience, scalability, security and privacy

• Integration of humans (in-the-loop and in-the-mesh)

• Smooth migration from existing running systems

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In a short …

• CPS assumes a key role to realize Industrie 4.0

• MAS is a suitable approach to implement the distributed intelligence in CPS

• MAS is being applied in industrial environments through several European R&D projects

• Important notes:

– Hide complexity, reveal functionality

– Think globally, act locally

• A long and hard path is needed to face several challenges and open questions

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Some open questions for research

How to properly design SOMAS systems?

How hierarchies are dynamically formed, evolved and removed?

How optimization is achieved in decentralized systems?

How to control nervousness in self-organized systems?

How to ensure the emergence of desirable behaviours?

And so many others …

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Thank you!

e-mail: pleitao@ipb.pt

URL: http://www.ipb.pt/~pleitao