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ENHANCING THE PERFORMANCE OF COLLABORATIVE ROBOT
ASSEMBLY SYSTEMS THROUGH STANDARDIZATION AND
COMPLEXITY MODELING
Normatov Islomiy
Ph.D. student of 05.02.04 “Standardization and Management Quality of product”
Andijan State Technical Institute, Andijan, Uzbekistan
https://doi.org/10.5281/zenodo.15906715
Abstract
This paper presents an approach to improving the performance of
collaborative robot (cobot) assembly systems by integrating international
standards and complexity analysis methodologies. Using a UR5 collaborative
robot equipped with object detection cameras, experimental tasks involving the
assembly of modular components were evaluated under varying complexity
scenarios. The Lucas method was used to quantify product and process
complexity through C1 (component complexity), C2 (assembly connection
complexity), and C3 (topological complexity). The study explores how
standardization—through ISO 9001, ISO 10218-1/2, and ISO/TS 15066—can
enhance safety, quality, and performance consistency in human–robot
collaborative environments. The results support the use of standardization as a
critical factor in optimizing assembly systems aligned with Industry 4.0
principles.
Keywords:
Collaborative Robots, ISO Standards, Human–Robot
Collaboration, Lucas Method, Complexity Modeling, Performance Systems,
Object Detection
Introduction
The transition to Industry 4.0 in modern manufacturing demands
intelligent and adaptive systems where robots and humans collaborate in real-
time environments. Collaborative robots (cobots) play a crucial role in enabling
flexibility, productivity, and customization in such systems. However, to ensure
these systems remain safe, consistent, and efficient, the implementation of
international standards and performance models is essential [1].
This study focuses on improving the performance of collaborative robot
assembly systems by integrating standardization and complexity modeling.
Using a UR5 collaborative robot equipped with object detection cameras, a
series of experimental tasks of varying complexity were performed. The goal
was to evaluate how standardized methodologies affect the system’s
performance in terms of quality, time efficiency, and operational consistency.
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Methodology
A UR5 collaborative robot was employed to conduct assembly operations
involving LEGO Mindstorms modules. The robot was programmed to manipulate
and assemble components with varying physical and connection properties. The
environment was designed to simulate a flexible, semi-automated assembly
station commonly found in educational and small-scale industrial laboratories.
The system included:
UR5 robot arm with a two-finger gripper
Object detection camera mounted to support part localization and
identification
Worktable divided into zones for component picking, orientation, and
joining
To analyze the assembly difficulty, the
Lucas method
[1] was used. This
method divides complexity into three main dimensions:
C1 – Complexity of product components
: determined by the size,
fragility, and variation of the parts
C2 – Complexity of assembly connections
: based on the number, type,
and precision required for joints or insertions
C3 – Topological complexity
: influenced by the spatial arrangement and
orientation requirements of the product
Three product variants were created with increasing complexity (Option A
– Low, Option B – Medium, Option C – High). Each was evaluated based on the
number of parts and associated complexity scores.
To guide safe and efficient performance, three key standards were
referenced:
ISO 9001:2015
– Quality Management Systems [2]
ISO 10218-1/2
– Safety Requirements for Industrial Robots [3]
ISO/TS 15066
– Collaborative Robot Safety Requirements [4]
These standards informed the development of the task workflow, safety
zoning, sensor integration, and quality control procedures throughout the
experiment.
Result
The integration of object detection systems and international standards
into the collaborative robot assembly process yielded several clear
improvements in operational performance, process structure, and safety.
During the assembly of LEGO-based modules of varying complexity
(Options A, B, and C), several improvements[5] were consistently observed:
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Enhanced object localization
: The object detection camera enabled the
UR5 cobot to identify and correct part positions, reducing the need for manual
adjustments.
Improved alignment
: Visual feedback allowed the robot to orient
components correctly before insertion, especially in tasks with higher
topological complexity (C3).
Reduced task interruptions
: The ability to detect missing or misplaced
parts in real time allowed the system to either retry or flag an issue for
intervention, minimizing dead-time during assembly.
These outcomes confirmed that the integration of vision systems supports
the successful handling of increased assembly complexity.
By adhering to
ISO/TS 15066
standards during the setup and operation of
the HRC system, the following safety improvements were noted:
Proximity responsiveness
: The robot’s operating speed dynamically
adjusted based on the detection of nearby human operators, contributing to a
safer workspace.
Controlled interaction zones
: The workspace was divided into defined
zones, consistent with ISO 10218 guidelines, to prevent unsafe overlaps
between human and robot operations.
No safety incidents recorded
: Throughout testing, no unexpected
collisions or unsafe behaviors were observed, validating the effectiveness of the
implemented safety protocols
In line with
ISO 9001
, the following quality management principles were
successfully embedded into the experiment:
Defined checkpoints
: Key stages of the assembly process were monitored
to detect deviations or performance issues.
Error tracking
: All task failures (e.g., misgrips, misalignments, incomplete
insertions) were documented for traceability and system refinement.
Repeatability across trials
: The process produced consistent outcomes
when repeated under the same complexity level, suggesting strong process
stability.
These results support the value of combining complexity analysis (Lucas
Method), vision-based control, and standardization as a structured model for
collaborative robot performance enhancement.
Disussion
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The structured integration of international standards and the Lucas
complexity model provided a reliable framework for evaluating collaborative
robot systems. Key insights include:
Standardization ensures consistency
in how tasks are designed,
monitored, and evaluated. ISO 9001 principles helped establish repeatable
quality inspection points, while ISO 10218-1/2 and ISO/TS 15066 guided the
safe layout and operation of the human–robot space.
Complexity modeling supports task planning and resource allocation.
For example, Option A (low complexity) involved mostly large components with
simple connections, while Option C included small components with tighter
tolerances and more intricate spatial arrangements. By quantifying complexity
using C1, C2, and C3, the experiment allowed for better prediction of assembly
time and error likelihood.
Object detection integration increased task adaptability.
The use of
cameras allowed the robot to adjust its approach based on part location and
orientation, reducing the impact of misalignment and improving the
repeatability of operations in higher-complexity tasks.
Performance tracking becomes more meaningful when guided by
standard metrics.
Rather than focusing solely on speed or output, the system
was evaluated based on structured criteria such as assembly precision, grip
success, reorientation effort, and part-handling stability.
Though detailed quantitative results are beyond the scope of this paper,
observational data confirmed that performance degraded as complexity
increased. This reinforces the importance of balancing task design with robot
capability and safety protocols—something achievable only through standard-
based design.
Conclusion
This study confirms that collaborative robot assembly systems benefit
significantly from the integration of
international standards
and
systematic
complexity evaluation
. The Lucas Method provided a structured way to assess
product and assembly challenges, while ISO 9001, ISO/TS 15066, and ISO
10218-1/2 offered a foundation for quality assurance, risk management, and
safety in human–robot collaboration.
By combining intelligent perception (object detection), structured task
modeling (Lucas Method), and international best practices (ISO standards),
manufacturers and educators alike can develop high-performing, scalable, and
safe cobot systems suitable for modern production environments.
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Future research may focus on integrating AI-based complexity prediction
and expanding the use of digital twins to simulate assembly performance before
physical deployment
References:
1. Lucas, J.M. "The Lucas Method for Design Complexity Analysis." International
Journal of Product Development, vol. 5, no. 3, 2009.
2. ISO 9001:2015. Quality Management Systems – Requirements. International
Organization for Standardization.
3. ISO 10218-1:2011 / ISO 10218-2:2011. Robots and Robotic Devices – Safety
Requirements for Industrial Robots.
4. ISO/TS 15066:2016. Robots and Robotic Devices – Collaborative Robots –
Safety Requirements.
5. I. Normatov. B.Asqarov. “OBTAINING SCIENTIFIC RESEARCH RESULTS ON
THE METHODOLOGY FOR IMPROVING THE PERFORMANCE SYSTEM OF
COLLABORATIVE ROBOT ASSEMBLIES BASED ON STANDARDIZATION”.
Western European Journal of Modern Experiments and Scientific Methods, vol.
2, no. 12, Dec. 2024, pp. 26-29,
