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1. 2020 IEEE 7th
International Conference on Engineering Technologies and Applied Sciences (ICETAS)
978-0-7381-0504-8/20/$31.00 ©2020 IEEE
Cobot Fleet Management System Using Cloud and
Edge Computing
Bukhary Ikhwan Ismail
Advanced Computing Lab
MIMOS Berhad
Kuala Lumpur, Malaysia
ikhwan.ismail@mimos.my
Hishamadie Ahmad
Advanced Computing Lab
MIMOS Berhad
Kuala Lumpur, Malaysia
hishamadie.ahmad@mimos.my
Mohammad Fairus Khalid
Advanced Computing Lab
MIMOS Berhad
Kuala Lumpur, Malaysia
fairus.khalid@mimos.my
Mohd Nizam Mohd Mydin
Advanced Computing Lab
MIMOS Berhad
Kuala Lumpur, Malaysia
nizam.mydin@mimos.my
Rajendar Kandan
Advanced Computing Lab
MIMOS Berhad
Kuala Lumpur, Malaysia
rajendar.kandan@mimos.my
Ong Hong Hoe
Advanced Computing Lab
MIMOS Berhad
Kuala Lumpur, Malaysia
hh.ong@mimos.my
Abstract— Contemporary manufacturing systems are still
evolving. Currently, the industry progresses from basic
mechanical assist systems to advanced automation such as the
use of Collaborative Robot. This paper presents an early
concept and proposal of Cobot Fleet Management System that
manages multiple COBOT on factories. The goal is to automate
and simplify the development and maintenance of COBOT. The
management system is a dual-sided architecture, cloud
centralized control and management of smart equipment and
secondly, edge computing that bridge between the
manufacturing floors and the application residing in the cloud.
We provide health data and automation of selected maintenance
task for COBOT by providing error notification, predictive
maintenance and production output visualization.
Keywords—COBOT, Fleet Management System, Docker,
Cloud Computing, Edge Computing, Smart Manufacturing,
IR4.0, Industrial Revolution 4.0
I. INTRODUCTION
In Malaysia’s manufacturing industry, SME accounts for
98.5% of the establishment. It is vital for SMEs with a limited
budget and funding to adopt Industry Revolution 4.0 (IR4.0)
to be continuously relevant in the global competitive industry.
Malaysia's Ministry of Trade and Investment (MITI) has
spearheaded a policy called Industry4WRD under its IR4.0
programme to address the transformation needs of local SMEs
[1].
As of now, the adoption rate of IR4.0 is slow due to the
lack of IT, automation, and robotics awareness among SMEs
in Malaysia. The impression of high investment and long
return of investment hinders the adoption. Without proper
knowledge and awareness, SMEs are slow to adapt IR4.0.
MITI has undertaken the necessary steps by defining key
strategies to stimulate adaptation.
To adopt new technologies and smart factory equipment,
SMEs will face new challenges. Challenges include hiring,
training, and getting work skill staff to implement, operate and
maintain smart devices. In this paper, we present a solution to
support the implementation and operation & maintenance
(O&M) for smart devices. We propose a Fleet Management
System to implement, operate and maintain Collaborative
Robot (COBOT).
The goal of the platform is to automate and simplify the
development and operation task in due course reduces the
labour cost. The COBOT Fleet Management System (CFMS)
is a dual-sided architecture. 1) Cloud; for centralized control
& management of smart equipment and 2) edge computing; a
compute node that acts as a bridge between the manufacturing
floors and the application residing in the cloud. We target two
sets of users. System Integrators (SI), a user that deploy &
customize COBOT solution and the manufacturer, user that
operate and monitor its smart manufacturing assets.
The CFMS platform will handle two areas. First, in the
design stage, CFMS provides the necessary tools and
environment in designing manufacturing cell. SI &
manufacturer can design, verify through simulation and
estimate the cost. Second is in the O&M stage – CFMS
reduces operational labour overhead and cost by having
unified & centralized management. We provide automation of
selected maintenance task for COBOT by providing error
notification, predictive maintenance and production output
visualization.
II. BACKGROUND
A. Industry Revolution 4.0
IR4.0 transforms the base process of product design,
fabrication and usage of the product. It revolutionizes how
manufacturing operates, maintain and perform service.
Information Technology is an important area in IR4.0 that
drives the digitalization of manufacturing. The convergence
of Information Technology with Operational Technology
forms the crux of Smart Manufacturing. It works beyond
automation and machine-to-machine communications.
Among the benefits of IR4.0 in manufacturing operation and
maintenance are enhanced efficiency in production, mass-
customization of product, defect-free, zero downtime, extends
tool life and energy efficiency [2].
B. IR4.0 in Malaysia
For the past 5 years, manufacturing industries have
contributed 22% to Malaysian GDP and 42% of employment
in Malaysia [1]. IR4.0 creates digital disruption trends where
it provides an opportunity as well as threats to the industry [3].
IR4.0 encourages productivity, increases production output
and reduces our dependency on foreign labour [4]. The
establishment that fails to adopt the trend may suffer from

2. being irrelevant, practising obsolete manufacturing processes
and possibly fail in creating new innovative products.
MITI has undertaken a task of setting up the IR4.0 under
National Policy back in 2018. The main objectives are to
enhance productivity, job creation, innovation, create skill-
talent, which will eventually create economic prosperity. To
achieve these objectives, Industry4WRD provides two main
key strategies, those are; 1) encourage knowledge
dissemination, 2) provide funding to both R&D and IR4.0
adaptation grants for industry players, 3) to create an inclusive
programme that ensures SME can forgo the transformation of
advance manufacturing processes. Given that 98.5% of
manufacturing in Malaysia consist of SMEs, Industry4WRD
policy provides the opportunities for SME of today, to become
giant of tomorrow [5].
C. Edge Computing In IR4.0
In AI, cloud computing has made a complex time
consuming deep learning possible. The latency in data
transmission and network communication that drives the
demand for edge computing [6]. The accelerated decline in
computing costs, compared to network drives demand for
moving intelligence nearer to the data source. Computing
requires high CAPEX, while operational networking cost
more in a long run. Thus, making more sense to put compute
power near to the edge. In future, compute deployment will
shift from macro to micro-build data centres, where 10 servers
or less will be in close proximity, modular and mobile [7].
Open source edge economy will emerge, where
applications can run on their own, while other companies own
the infrastructure. In manufacturing, by using edge-computing
technology closer to the shop floor or machines, it can provide
greater and faster response time for delivering actionable
analytics to the manufacturer [1].
D. COBOT
Collaborative robot (COBOT) is a mechanical device that
manipulates objects. It is one of a smart device suitable to be
adapted in IR4.0. It is designed to share the same workspace
with humans making collaboration between the two possible.
COBOT can assist in a complex task that is not fit for full
automation. It is a lightweight device as compared to an
industrial robot and can be placed and relocate easily [8].
With COBOT, the human-machine collaboration helps a
person with challenging, repetitive, and complex activities
while protecting the worker from health or work injuries. For
example, it can perform a more accurate assembly related
activities compared to a human. The anticipated benefits of
using COBOT are the increase in productivity, improved
workspace condition in terms of ergonomics and safety. It is
suitable for small to mid-sized industry needs.
III. REQUIREMENTS FOR FLEET MANAGEMENT SYSTEM
Here we discuss the requirements for COBOT Fleet
management system. We discuss with potential system
integrator & customers to understand the needs and
operational challenges in managing COBOT. Our high-level
requirements as follows: -
1) Design & simulation – support engineers in designing
a new production cell that uses COBOT.
2) Visualize equipment health – support engineer in
getting the health status of each equipment.
3) Zero downtime as a requirement – provide features and
functionality that anticipate failure before it occurs & possible
preventive measures suggestions.
4) Data for Operation Equipment Effectiveness (OEE) –
provide granular information for OEE in an individual
COBOT production cell.
5) Schedule maintenance – Currently COBOT is service
periodically e.g. quarterly or yearly. Among them are visual
inspection, COBOT arm calibration and critical software
update. Engineers require a system that recommends future
service work.
Based on high-level requirements mentioned, there are
two distinct areas of requirements 1) to assist in design &
prototyping process and 2) to ease the deployment, operation
& maintenance. We translate the above into technical
requirements to further detail the proposal. The technical
requirements are -
1) Design & Simulation
A cloud-based solution that supports the activities of
design & prototyping. To host open-source design &
simulation software and utilize our existing shared services
e.g. IaaS, shared storage, AI facilities and others.
2) O&M - Data Collection
A mechanism to collect data from multiple on-site
COBOT and push it to the cloud. Having rich datasets provide
valuable insight into the health of the assets.
3) O&M - Preventive & guided maintenance
A feature that notifies and suggest actionable tasks.
Provided by data collection module, analytics and prediction
are possible. We can discover Interesting insight from simple
rules or machine learning technique.
4) O&M - Deployment management
To manage the deployment of COBOT, there are two sets
of users. First is a system integrator that maintains factory
equipment during warranty. Second, is the manufacturer
engineers that operate & maintain multiple COBOT. This
feature shall allow the deployment of firmware as well as the
software of COBOT.
5) Non-functional requirements
To support future expansion adhered to industrial
standards, we abide by several non-functional but essential
requirements. 1) Security, the system must use encryption,
authentication and ensure data confidentiality. In an area of
cloud and edge, it is crucial to protect each manufacturer’s
data. 2) Enable bi-directional communication for future usage.
By having an architecture that supports it, future expansion
can be introduced easier. 3) The solution needs to conform to
industry standard. For examples, adhering to communication
& protocols standard for interoperability. Expandability, the
system must able to grow and communicate with other
industrial devices such as camera, PLCs, sensors and
actuators.
IV. PROPOSED ARCHITECTURE
We plan to monitor the production cell from the cloud.
Each production cell consists of COBOT arm, manipulator

3. controller, gripper and other equipment attached [9]. An edge
server is placed on-site to perform message parsing of
incoming data and perform AI inferencing for faster response
and action.
Based on Fig. 1, here a list of proposed components:-
1) Cloud Consist of the following components: -
• Application Dashboard – to visualize monitoring and
health data of factory equipment.
• Application Design & simulation hosting - SaaS-based
design & simulation software hosted in IaaS.
• Application InfluxDB – to store time-series data.
• Application Cloud MQTT instance – subscriber for all
Edge MQTT instances to get health data. Publisher for
COBOT deployment & operation instruction
• Application Analytics – to perform machine-learning
analytics for creating AI model[10].
• Mi-Focus Container Management – To host all the
above application as well as on edge site
• Mi-Focus Registry - Docker Image Registry to host
application, AI model & firmware updates.
• Mi-Cloud – to host VM for containers
• Mi-Ross storage - to store, VM & data and relevant to
design & simulation artefacts.
2) Edge
• COBOT – a pair of gripper, arm, and controller.
• Edge Server - communicate with factory equipment
e.g. PLC, PC, COBOT. One server communicates with
multiple COBOT on site.
• Intermediate storage – host temporary data in event of
internet disconnection.
• Edge MQTT instance – publisher of data collected
from factory equipment & subscriber to cloud MQTT
for instruction.
Fig. 1: Overall System Architecture
3) Supporting Technologies
• Docker – All the applications, is running as a container
for ease of deployment and manageability of the
software component. Analytics will run within edge
server, for real-time analytics and notification to shop
floor using Andon.
• Centralized Image management – Using Docker image
to package application and AI model. Docker images,
have delta differences enabled using Union file
system. It makes it easier for version control and
rollback procedure. The repository is for both cloud
and edge deployment.
The platform relies on two communication mechanisms.
Push, we push monitoring data from the edge to cloud. Pull,
edge devices pull the latest command using MQTT subscriber
for execution purposes. We use NodeRed (NR) a message-
parsing software on Edge Server. Using third party extension,
we can enable standard manufacturing protocol such as
EtherCAT, ModBus, SNMP or Siemen S7. In our prototype,
we use ModBusTCP to collect COBOT specific metrics.
To communicate with factory equipment, we rely on
ModBusTCP communication. Our current proof of concept
Neuromeka COBOT, support up to 32 concurrent connection
with 10ms or 100Hz max communication data reading cycle
[11]. SNMP and Syslog are used to collect OS and HW related
information from the Manipulator Controller. From here, we
push data to the local DB with Edge Server for caching
purposes and route to MQTT publisher for Cloud MQTT
subscriber to fetch the data. Using MQTT, we can sit behind
the factory firewall, without exposing any services to the
internet. We prefer using MQTT as opposed to GRPC for its
simplicity and becoming the norm for industrial standard
protocol.
Once data reaches the cloud, we will ingest and store
metrics into appropriate storage e.g. time-series DB,
Elasticsearch and MySQL. Data can be visualize using
Grafana. Our propose solution is to leverage internal existing
services, such as IaaS, shared storage, edge computing and
other virtual infrastructure services. All application on the
edge server uses container. On the cloud, we run either
container or Virtual Machine. A solution stack that is generic
and extendable for other smart equipment. It has to be
customizable, modular and flexible to be able to meet the
demands and constraints of each factory.
V. PROPOSED SOLUTION
Here we detail out our propose solution.
A. Design & Simulation
We host and run on-demand design & simulation software
such as Gazeebo and CoppeliaSim. Using our previous
matured infrastructure, we leverage our shared services such
as IaaS Mi-Cloud; to host either container of virtual machine
powered with GPU feature.
Any design & software artefact can be store in our Mi-
Ross shared storage that supports file versioning and exposes
the data using S3 or NFS protocol. This encourages design
reusability and easy access by users. By hosting these tools,
we can create on-demand SaaS like offering to system
integrator and customer that require design & simulation
capability. Thus, reducing up-front investment for SME for
industrial design purposes.

4. Fig. 2: Data Collection & Users
Fig. 2 shows the monitored components that we plan to
manage and its potential users e.g. System Integrator & End
Users. We collect COBOT operations data, environmental
metrics and data on production output. The goal is to collect
relevant metrics to notify and recommend actionable actions
to engineers more effectively.
TABLE I: DATA COLLECTION LIST
Areas Description COBOT specific Examples
Asset
tracking
Asset encompasses
physical as well as
virtual/soft attributes of
the equipment. To track
& maintain the device in
optimum condition
Physical - model, asset ID,
manufacturing date, date of
commencing
Virtual - software, OS,
application, Cobot’s Program.
Device
status &
health
Operational Status. To
observe & maximize the
effectiveness of device
utilization
Events - Start, stoppages, halt,
job status, errors, collision.
Operational metrics -
movement, gripper, spin counts,
logs, motor load, temperature,
COBOT movement –
coordinate, speed, velocity.
Environmental
conditions. Data that can
influence machine
lifetime or even
COBOT’s task.
Temperature, humidity,
vibration (hits), power
consumption, current load
Producti
on Status
Product & job status.
Data which provide
productivity rate
calculation
Program status, current
task/sequence status, &
duration.
Table I shows COBOT tracking, status, health, operational
and, production data. These data provide operational status
information to the COBOT arm.
Asset tracking - we collect hardware as well as software
information. Engineers can use this information to track or
check the latest information on COBOT.
Fig. 3: Cobot arm position & current program sequence
We collect COBOT arm movement. These data are used
to visualize near real-time COBOT movement on our
dashboard as shown in Fig. 3, the COBOT arm represents with
base, shoulder, elbow and wrist. Each with angle, velocity,
torque, speed and acceleration data.
To check the status of production cell in higher
granularity, we collect the data of program & individual task
within the program itself. We collect the task status,
completion as duration to complete. From these data, we can
see any stuck program or task and possible deviation of task
duration. If any anomaly is detected, we will notify it to the
engineers. These data can be used to determine individual
manufacturing cells productivity.
We capture the environmental data, to determine the effect
on the factory equipment. Temperature, humidity, vibration,
current load are some of the external stimuli that might upset
the equipment. We then compare to the manufacturer’s
recommended operation standards and notify if it is beyond
the threshold or recommended environmental condition.
VI. O&M PREVENTIVE & GUIDED MAINTAINANCE
Among the goals of IR4.0 are 1) to optimize machine
utilization and 2) to increase the life span of assets. IR4.0
advocate that every assets and system connected and
eventually unified. By interaction within and between system
and devices. It creates value-added features such as failure
prediction, self-configuration and a possibility of creating a
real-time adaption to changes.
Currently, each production cell is a black box. A single
PLC monitors multiple production units as a single cell. If one
unit is down, it does not provide detail error or fault about the
cell. More granular monitoring is needed e.g. a sequence of
the current task, duration and status. This enables us to track
any anomaly, e.g. duration for each task compared to baseline.
A live warning notification on site can minimize the
damage to the assets. Real-time monitoring and analytics at
the edge can prevent damages to the factory asset. Both
notification and recommendation alert should be done near the
shop floor or edge per see for fast response time. Intelligent
predicted notification could be a trigger for advance schedule
maintenance. A proper downtime window for production can
then be plan and execute by an onsite engineer. Thus, lowering
the impact on production operation.
Schedule maintenance for example would be COBOT arm
calibration notification. We will collect COBOT arm
movement and degree of COBOT arm during zero position as
a baseline figure. During non-production hour, the COBOT
arm will rest at zero position. We then check for any deviation
with the current degree and baseline figure. Any misalignment
will trigger a notification to engineers. This act as an
actionable item for preventive maintenance where system
prompt for calibration. Other service notification would be
critical firmware or software update. A proactive and
preventive notification can contribute to zero-downtime
value.
A second example would be, to create a rule base model.
Based on specific manufacturer recommendation e.g. MTBF
of COBOT’s components, we can count the number of motor
rotation or collect environmental metrics e.g. temperature,
vibration or humidity. If the current value is near the threshold,
the system will prompt the engineers. A third method would
be to capture errors and map the precursor events or trends as
pre inputs. By identifying precursor events; we might capture
the cause of the error.
Since all data resides in the cloud, the model can be trained
and created on the cloud itself. The edge server is much more
suited to perform the AI inferencing giving real-time output.
Sensors Software
Robot
A
Grippers Other
System Integrators
End Users (Manufacturers)
Users
Monitor
components

5. VII. O&M – DEPLOYMENT MANAGEMENT.
Deployment management handles COBOT’s program,
firmware and OS updates and AI model deployment. For
example, critical or security patch is important to ensure the
assets are running smoothly.
Engineers need to deploy and maintain COBOTS.
Deployment module will ease the engineer’s task to ensure the
latest firmware updates & security patch for multiple
customer’s sites. We need to deploy, maintain the version,
tracking and possibly introduce rollback functionality.
Managing through policies will ease the engineers in
managing large scale or multiple site deployment.
Here we plan to manage the deployment remotely. A
centralized cloud-based CFMS where multiple sites, factories,
or COBOTs can be managed together through its respective
edge devices. An edge device act as intermediator or bridge
between factory equipment and cloud. It relays the
deployment & update instruction to COBOT.
We utilize Docker image as a form of software delivery
and auto-installation. Using Docker registry, remote edge
server can pull the images. By using the container technology,
it eases the deployment as well as contain the software
perfectly on the edge server. Docker supports image
versioning, rollback features, and self-installation with proper
scripting. Once application or software is pulled to the edge
server a self-initiated script will run and install the application
on edge server or update the software components within the
COBOT itself.
VIII.FUTURE WORK
By capturing the relevant process, environmental and
robotic arms data, we could monetize and create insightful
trends and model. These unique insights can be capitalized
and sell to other companies with similar manufacturing
process or products.
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