=Paper=
{{Paper
|id=Vol-3214/WS3Paper5
|storemode=property
|title=Reference Models and Standards for the Integration of Mobile Robotics for Internal Logistic Applications
|pdfUrl=https://ceur-ws.org/Vol-3214/WS3Paper5.pdf
|volume=Vol-3214
|authors=Arcadio García,Francisco Fraile
|dblpUrl=https://dblp.org/rec/conf/iesa/GarciaF22
}}
==Reference Models and Standards for the Integration of Mobile Robotics for Internal Logistic Applications==
https://ceur-ws.org/Vol-3214/WS3Paper5.pdf
Reference Models and Standards for the Integration of Mobile
Robotics for Internal Logistic Applications
Arcadio García 1 and Francisco Fraile 2
1
EXOS Solutions, Camino de Vera s/n, 46022, Valencia, Spain
2
Universitat Politècnica de València, Camino de Vera S/N, Valencia, 46022, Spain
Abstract
This article discusses different methods, reference models and standards that can be applied
to achieve vertical integration of mobile robotics for internal logistics applications. To realize
this vertical integration, we will first consider the functionalities of current enterprise
information systems, i.e., ERP, MES, MON and WMS among others, that support production
and logistics operations, and then, by applying the ISA-95 hierarchical model for enterprise-
control integration, with its different functional levels for enterprise information systems, we
will provide a standards-based framework to support the integration of AMR mobile robot
fleets and the described enterprise information systems.
Keywords 1
Reference models, automated mobile robotics, integration
1. Introduction
Automated Mobile Robots (AMRs) have recently emerged as an alternative to Automated Guided
Robots (AGRs) to automate internal logistics operations. AMRs do not require dedicated space to
operate, and in this sense, they represent a quite interesting alternative for companies and logistic
processes where it is not feasible to deploy AGVs. AMRs are more flexible and collaborative by
nature. Unlike AGVs, AMRs support free-navigation and can navigate in collaborative spaces in a
safely manner. The additional degrees of freedom come at the expense of operations management
complexity, and the Fleet Management System (FMS), which is the component in charge of managing
the operations of the robotic fleet needs enhanced functionalities compared to its AGV counter-part.
In this context, many enterprises and particularly small enterprises use legacy information systems
to support business processes, sometimes packaged in custom Enterprise Resource Planning (ERP)
solutions, or distributed across ad-hoc files or databases (e.g., Microsoft Excel files or Access
databases). The same applies to production and logistics operations management. Consequently, it is
critical to have the right strategy to achieve a good integration of mobile robotics with these legacy
systems, as this integration is critical to streamlining business processes and maximizing the benefits
of logistic tasks automation. An inefficient integration can introduce delays in operations and neglect
the benefits of automation in the first place. This paper presents different standard-based methods and
models to facilitate the integration between the operational level of mobile robotics, namely the FMS
and other information systems supporting other management functions in the organization, mainly
ERPs, but also other information systems that provide support to manufacturing and logistics
operations, like Manufacturing Executing Systems (MES) or Manufacturing Operations Management
(MOM), and Warehouse Management Systems (WMS).
The paper focuses first on the definition of the functionality of each system and the type of
information that needs to be exchanged with the FMS. Later, the paper presents and discusses a
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: arcadio.garcia@exos-solutions.com (A. García); ffraile@cigip.upv.es (F. Fraile)
ORCID: 0000-0003-0852-8953 (F. Fraile)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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taxonomy of intra-logistics tasks based on interoperability standards, and some technical aspects of
the implementation of the required interfaces between systems.
2. Mobile robotics integration approach
To provide a standard based framework to support the integration of AMR fleets, the proposal
described in this paper adopts the ISA-95 enterprise model [1]. The ISA-95 is a well-known
hierarchical model for enterprise-control integration. ISA-95 defines different functional levels for
enterprise information systems. Level 4 – Business planning and logistics establishes the basic
scheduling for production, material use, delivery and shipping and inventory levels. The planning
spans a time horizon of weeks-months. Level 3 – Manufacturing Operations Management and control
implements functions to perform a fine grained, detailed scheduling of operations in shorter time
horizons, and functions to control and optimize the production process, and finally, levels 2 to 1
implement the low-level control functions that drive the production process. Figure 1 illustrates the
adopted model. The figure shows some of the most relevant functions in each level, together with the
acronym of the system that commonly implements the function.
Figure 1: ISA-95 enterprise-control integration model for AMR FMS
From left to right, in level 4, the diagram identifies master data, as the function to master or edit
enterprise data relevant to the larger enterprise architecture. This master data comprises data
describing products, resources, and processes, and is associated with the ERP, as in practice, for most
companies, this is the preferred source of truth that other systems should use as a reference. Other
relevant functions in this level are the Master Production Schedule (MPS) which determines the
resources (production, staffing, inventory) that need to be provisioned in every time period of the
planning time horizon. The MPS is linked to manufacturing and logistics operations among others,
determining what needs to be produced – Manufacturing Resource Planning (MRP II) - and the
material resources that need to be provisioned - Material Resource Planning (MRP) – among others.
The diagram also identifies advanced analytics functions, providing in-depth insights to support
strategical and tactical decisions at the enterprise level, and data management functions to control
enterprise wide data management functions, normally available in Business Information (BI) systems.
Similarly, the diagram depicts in level 3 several information systems specialized in operations
management and control, namely the Manufacturing Executing System (MES) – manufacturing
operations -, the Warehouse Management System (WMS) – warehouse operations, the Maintenance
Information System (MIS) – maintenance operations or Laboratory Information Management System
– quality operations. ISA-95 (Part 3) [2] defines activity models for operations management
(production, maintenance, inventory, among others), which are instances of the generic activity
model, meant for work management in general. FMS activities can also be described as an
instantiation of the generic model, defined for the management of AMR operations. The bullets below
summarize the definitions of the level 3 activities in the generic model and how they are instantiated
in AMR fleet management:
- Definition management: Management of level 3 information describing operations (e.g.
instructions, recipes, dependencies between data elements) including definition and
maintenance of data and KPI management. For FMS, this includes definitions of the logistic
tasks that can be performed by the AMRs and how they are decomposed into low level robot
commands.
- Resource management: Management of information describing resources (machines, tools,
labor, skills, materials, energy) required in operations. The main types of resources regarded in
FMS are AMRs (description of their skills in terms of the tasks they are able to perform, the
modules they implement), the location and navigation properties of storage zones and other
work areas overall navigation areas. Formally, AMRs are storage units (equipment to move
and handle material) and as such part of the equipment hierarchy.
- Detailed scheduling: Determine the optimal use of local resources to meet the requirements
of the production scheduling (possibly generated in level 4). It consists of a fine-grained
collection of work orders assigned to resources in a specific sequence. In an FMS, these
activities span the optimal assignment of logistic tasks to robots and routing of the fleet
through work areas to ensure the production scheduling generated in level 4.
- Dispatching: Management of the workflow by dispatching work to specific work and
personnel, in the sequence in which the work needs to be done according to the detailed
schedule. The FMS should therefore implement functions to enable the dispatching of logistic
tasks to robots, decomposed into low level commands.
- Execution management / control: Coordination of the processes (manual and automated) to
ensure the correct execution of the work plan according to the accepted quality standards. For
AMRs, this means the detection and prevention or correction of blocking situations (e.g. low-
battery, deadlock situations) that may occur in the execution of the detailed work plan and re-
allocate tasks or change the routing as needed to ensure the accepted quality standards (in
terms of overall execution time, safety, or efficiency).
- Data collection: Collection, management and retrieval of data modeling the execution of the
work (e.g. including sensor readings, status, events, operator actions). In an FMS, collected
data includes the timestamped location and geo-localized status information (current mission,
completion percentage, battery level, etc.) of the fleet.
- Performance analysis: Analyze and report performance information to level 4 system. Trace
the execution of the work and provide KPIs and analysis of equipment, materials, or personnel
involved. In an FMS, this involves the processing of collected data to analyze the performance
of the robotic fleet in terms of relevant KPI definitions for logistics operations management.
- Tracking: Prepare the responses for level 4 systems, including summaries and reports about
the finalization of operations, as well as any other relevant information such as performance
analysis results.
ISA-95 also provides a taxonomy of operations, which can be used as a basis to define the types of
internal logistics operations that can be delegated to the robotic fleet. Mainly other operations may
provide the direction for internal logistics operations, for instance a production operation may provide
a picking order for work preparation or material handling. From the definitions in ISA-95, we identify
the following classification of tasks for the AMR fleet:
• Manufacturing operations support tasks: Picking and transportation of materials and
tooling for work preparation, retrieval and storage of products.
• Maintenance operations support tasks: Collection, transportation and retrieval of spare parts
and tools for maintenance operations.
• Inventory operations support tasks: Inventory of materials and products. Release to ship.
• Quality operations support tasks: Collecting quality control samples and transportation to
quality laboratory units.
The alignment with ISA-95 facilitates the integration of standard-based interfaces and data models.
For instance, the Business to Manufacturing Markup Language [3] is an Extended Markup Language
(XML) [4] implementation of the ISA-95 family of standards. The associated ISO 22400 [5] provides
definitions and methods to calculate KPIs. It is also important to note that the levels in the equipment
hierarchy of ISA-95 are also adopted by the Reference Architecture Model for Industrie 4.0 [6] as one
of its dimensional axes. However, the definitions in these standards are biased towards production and
some definitions need to be reviewed to be applied for internal logistics. The Supply Chain
Operations Reference Model (SCOR) [7] is another valuable reference for methods and definitions for
performance KPIs for AMR robotic fleets.
Although all these standards and reference models are very valuable, it is important to bear in
mind the high heterogeneity of interfaces and data models found in real-world small and medium
enterprises. To address this heterogeneity, the approach suggested in this paper is to adopt a mediation
strategy, establishing a middleware system or broker between the FMS and external applications,
mainly the ERP system, but also with other related systems in level 3 like the WMS or MES. To
integrate with a specific system with a legacy interface, like file exchange in proprietary file formats
or through Application Programming Interfaces (APIs), it is necessary to use an ad-hoc broker
specifically designed to integrate with the specific service through the specific interface.
3. Acknowledgements
This research has been funded by the Agència Valenciana de la Innovació, under the program
Proyectos Estratègics en Cooperació 2021 (INNEST/2021/226, INNEST/2021/210).
4. References
[1] International Electrotechnical Commission, IEC 62264-1:2013 Enterprise-control System
Integration – Part 1: Models and Terminology, 2013. URL:
https://www.iso.org/standard/57308.html
[2] International Electrotechnical Commission, IEC 62264-3:2016 Enterprise-control System
Integration – Part 3: Activity models of manufacturing operations management, 2016. URL:
https://webstore.iec.ch/publication/33511
[3] Manufacturing Enterprise Solutions Association, Business to Manufacturing Markup Language,
version 7, 2020. URL: https://mesa.org/topics-resources/b2mml/
[4] World-Wide Web Consortium, Extensible Markup Language (XML) version 1.1, 2012. URL:
https://www.w3.org/XML/
[5] International Standards Association, ISO 22400-2:2014 Automation Systems and Integration –
Key Performance Indicators (KPIs) for Manufacturing Operations Management – Part 2:
Definitions and descriptions, 2014. URL: https://www.iso.org/standard/54497.html
[6] M. Hankel, B. Rexroth, The reference architectural model Industrie 4.0 (RAMI 4.0), ZVEI, 2010.
URL: https://przemysl-40.pl/wp-content/uploads/2010-The-Reference-Architectural-Model-
Industrie-40.pdf
[7] R. G. Poluha, Application of the SCOR model in supply chain management, Cambria Press, New
York, NY, 2017.