=Paper=
{{Paper
|id=Vol-3214/WS3Paper4
|storemode=property
|title=Robotics Platforms for Internal Logistics: A Technical Architecture Proposal
|pdfUrl=https://ceur-ws.org/Vol-3214/WS3Paper4.pdf
|volume=Vol-3214
|authors=Francisco Fraile,Raul Poler
|dblpUrl=https://dblp.org/rec/conf/iesa/FraileP22
}}
==Robotics Platforms for Internal Logistics: A Technical Architecture Proposal==
https://ceur-ws.org/Vol-3214/WS3Paper4.pdf
Robotics Platforms for Internal Logistics: A Technical
Architecture Proposal
Francisco Fraile 1,2 and Raul Poler 1
1
Universitat Politècnica de València, Camino de Vera S/N, Valencia, 46022, Spain
2
Escuela de Empresarios, Muelle de la Aduana S/N, Valencia, 46022, Spain
Abstract
This paper presents a reference technical architecture model for robotic platforms specifically
designed for internal logistics applications. The reference application is based in industry
standards like ISA/IEC 62443 and the Industrial IoT Reference Architecture (IIRA).
Keywords 1
Autonomous mobile robots, internal logistics, reference architecture, fleet management
systems
1. Introduction
Autonomous Guided Vehicles (AGVs) are a well-established solution to automate internal logistic
operations [1]. AGVs have been designed to operate in large installations and consequently, they
require large spaces dedicated to robot operations. Human safety is thus achieved through physical
isolation: Humans and robots do not share the same space to avoid any risk of accidents that could
cause harm to human operators. These requirements on the other hand make it difficult to introduce
this technology in smaller logistic installations, as those typically found in medium or small sized
companies, which do not have enough space available to deploy AGVs, or that need instead a
collaborative solution that would allow for a scaled, progressive automation of logistic tasks. As a
response, Autonomous Mobile Robots (AMRs) represent a versatile alternative. Unlike AGVs, AMRs
are able to navigate freely in a space shared with humans, taking a more collaborative approach to
internal logistics automation.
In this sense, robotic platforms for internal logistics facilitate the deployment, planning,
management, and supervision of fleets of AMRs in large, medium-sized, or small enterprises, to
perform logistics tasks in industrial environments. Primarily, these platforms rely on the following
technologies to support internal logistic operations and management:
• Autonomous vehicles and collaborative robots for logistics applications: The robotic
platform will integrate with autonomous vehicles and collaborative robots designed to execute
logistics tasks in industrial environments. The robots will be able to execute tasks in the
internal logistics of companies, favoring the automation and efficiency of processes.
• Supervision and monitoring in the edge/cloud: The platform facilitates the deployment of a
set of services that will connect the physical elements (robots) with the management and
optimization logic, allowing decoupling the control aspects from the operations. These
services are rooted in cloud technologies to provide the required elasticity to deploy on-
premise or in- cloud depending on the specific user requirements.
• Fleet management and optimization: From a functional perspective, the main added value
of the services provided by the platform is to facilitate robot fleet management and
operations: Optimal planning and sequencing of operations, routing and monitoring of
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: ffraile@cigip.upv.es (F. Fraile); rpoler@cigip.upv.es (R. Poler)
ORCID: 0000-0003-0852-8953 (F. Fraile); 0000-0003-4475-6371 (R. Poler)
© 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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operations through indicators. The algorithms and analytical techniques used are integrated
into applications in a simple and efficient way using the Function as a Service (FaaS) cloud
computing paradigm.
• Blockchain and traceability in the supply chain: LogiBlock will leverage Industrial IoT
and blockchain technologies to provide the level of trust required by supply chain
collaborators in operations traceability.
The efficient integration of these technologies, together with other information systems already
present in factories, such as Enterprise Resource Planning (ERP), Manufacturing Execution Systems
(MES), or Warehouse Management Systems (WMS) potentially enables a turn-key solution with the
following features:
• Plug-and-Play: Communication services use standard communication interfaces and modules
to automatically discover and integrate with hardware and software systems in the factory.
The solution will scale transparently to the user as new vehicles and robots or modules are
added to those already deployed. For instance, the FMS can automatically discover and on-
board a new AMR, or dispatch a picking order to an integrated WMS.
• Scalability and modularity: The hardware required to enable robot navigation and
management is affordable, has a small footprint, and can be deployed in a cost-efficient way.
Services can be hosted on-premise or on-cloud, to simplify the connection and deployment of
the platform to end users solution, making the solution modular and scalable. End-users will
be able to easily add new modules to vehicles and robots to execute different logistic tasks or
to adapt them to different environments.
• Customizable: The solution will provide programming interfaces (APIs) and software
development kits (SDKs) to facilitate the integration of the service layer with other systems
and applications, and the development of customizations or adaptations to different verticals.
This paper presents a reference architecture of a robotic platform specifically designed for internal
logistics applications using AMRs and meeting the requirements of small and medium-sized
companies. The reference architecture is based on Robotic Operating System (ROS), an open-source,
modular platform for robotic applications [2]. The next describes the proposed reference architecture,
and section 3 describes the methodology used to develop usage, functional and technical
specifications based on the proposed reference architecture.
2. Technical architecture
Figure 1 illustrates the reference architecture for robotic platforms proposed in this research paper.
The reference architecture has been developed in the framework of the research project Logiblock,
and is based in known industrial best practices, security standards, and relevant reference
architectures like the Industrial Internet of Things Reference Architecture (IIRA) [3]. The main
objective of Logiblock is the development of a trustworthy robotic platform designed to facilitate the
introduction of AMRs in medium and small sized companies.
Figure 1: Reference Architecture Diagram
The definition of the reference architecture is based on industry standards and best practices for
open robotics, and it follows the recommendations of the ISA/IEC 62443 standard on security in
industrial control systems [4]. Specifically, IE 62443-3-3 "System Security Requirements and
Security Levels" establishes a separation between different security levels or zones:
• Level 0: Industrial level. This level groups together the physical components of industrial
systems, mainly actuators and sensors. In a robotic platform, this level groups the physical
sensors and modules that enable navigation, such as Laser Imaging Detection and Ranging
(LIDAR), Inertial Measurement Unit (UMI) or Ultra Wideband (UWB), as well as logistic
modules like cameras, laser scanners or encoders to detect products, e.g. for product
inventory. At this stage it is important to consider that for robustness and efficiency, in
practice, several indoor localization technologies are combined. UWB and IMU-UWB
integration provides a good trade-off between cost, accuracy and deployment complexity [5].
The base hardware selected to implement the reference architecture comprises ROS
compatible logistic robots like Robotnik`s RB-1 [6] (up to 30kg loading weight) and Theron
logistic robots [7] (up to 200kg loading weight).
• Level 1: Control. This level contains the system-level control elements of the components at
level 0. In a robotic platform, this level groups the Human-Robot-Interaction modules, the
modules to model the environment, including humans, and the modules to control the robot:
human navigation and motion planning, actuator control, and movement control. Modules for
data fusion to enable the integration of different indoor positioning systems are also located at
this level. From a technical point of view, the modules at this level are implemented as ROS
modules, interconnected in a level-1 ROS network, deployed in a edge platform or on-board
computer with GPU acceleration to achieve the required performance. This is particularly
important for modules that rely on neural networks, like (Yolov4) object detection [10] and
human activity prediction. The performance of these modules has been successfully tested
using a NVIDIA Jetson module [11]. Communication with higher layers is implemented
through ROS bridge modules (communication modules) that act as secure conduits to
exchange information with level-2 components, using the Message Queue Telemetry
Transport Protocol (MQTT) [8] or the OPC Unified Architecture (OPC UA) [9] protocol.
• Level 2: Operation. This level groups together the operation and supervision systems, such
as operator terminals or consoles, monitoring applications to monitor and control the fleet,
etc. In a robotic platform, this level groups the main functional blocks of the Fleet
Management system, including functions to control and orchestrate the robotic fleet, calculate
Key Performance Indicators (KPIs) for logistic operations monitoring, as well as functions to
enable communication between level 1 and higher levels according to security specifications
for Industrial Control Systems. From a technical point of view, these modules are
microservices [12] deployed in a microservice orchestration platform like Kubernetes [13].
The Machine 2 Machine communication module provides an endpoint (e.g. MQTT broker)
used to send control commands to and receive status feedback from the robotic fleet. The
information is stored in a time series database microservice to enable robot tracking and tasks
traceability. Moreover, the global navigation map used by the robots is processed to generate
a network graph used to implement routing and task sequencing functions. To facilitate
vertical integration, this level implements microservices to manage master data and expose
management functions to level 3 services or external systems through a management
Application Programming Interface (API).
• Level 3: Enterprise. This level groups together equipment and systems to provide support to
the company's business processes, such as ERP. This level of the reference architecture
groups functional blocks to implement Role Based Access Control to the FMS functions,
including federation with external authentication and authorization services, as well as
functional blocks to integrate with other enterprise systems, like the ERP, MES/MOM, or
WMS integration. From a technical point of view, these functions are mapped to
microservices. Communication with level 2 functional blocks is achieved through the
management API, so that level 2 and level 3 services are decoupled to achieve inter-level
isolation.
• Level 4: Supply chain. This level is introduced by the authors to extend the solution to the
supply chain level, enabling collaboration among supply chain collaborators. This level
groups advanced services to enable trustable supply chain traceability using blockchain
technology, data services to enable the integration and synchronization of data distributed
across different platforms, supply chain operations planning services, and simulation services
to simulate internal logistic processes.
3. Discussion
The ISA/IEC 62443 standard establishes that any industrial control system compatible with this
standard must conveniently define these levels, so that they can be located in independent sub-
networks. To adequately protect the components at the physical level, level 0, which are those that
can compromise the safety of operators, communications must always be made from the lower levels
to the upper levels (communications are not allowed to be initiated in the opposite direction), and all
communications between levels must use secure, properly protected conduits (security paths between
two levels). This allows for the proper establishment of a Defense In Depth strategy, which is a
defense strategy based on the establishment of different security controls to protect critical systems at
lower levels [14].
From a business perspective, the system must be as user-friendly as possible, easy to deploy and
therefore, its implementation in accordance with these standards must be simple, so as not to represent
a potential threat, but rather an advantage in the factories. Furthermore, the system must allow the
deployment of the different components in the edge/cloud continuum in a flexible way, allowing
some of the system's features to be offered in Software as a Service models to favor collaboration in
the supply chain. Thus, system components must be able to be deployed in a distributed manner
across different hardware equipment to enable the deployment of defense strategies compatible with
this standard. Based on these requirements, the proposed reference architecture has adopted a layered
model that is compatible with this vision and which translates into the described levels.
4. Acknowledgements
This research has been funded by the Agència Valenciana de la Innovació, under the program
Projectes Estratègics en Cooperació 2021 (UPV->INNEST/2021/226). Action co-financed by the
European Union through the European Regional Development Fund (ERDF) Operational Programme
for the Valencia Region 2014-2020.
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