International Journal of Engineering Insights: (2025) Vol. 3, Nro.1, Regular Paper

https://doi.org/10.61961/injei.v3i1.87

Performance Evaluation of ROS2 and Micro-ROS for

Real-Time Distributed Control of Omnidirectional Mobile

Robots

Jonathan S. Toapanta · Carlos Julio Fierro-Silva

Received: 08 Jul 2025 / Accepted: 15 Oct 2025 / Published: 15 Nov 2025

Abstract: Omnidirectional mobile robots require ef-

ﬁcient distributed control architectures to ensure reli-

able motion execution and real-time communication be-

tween high-level navigation software and embedded mo-

tor controllers. This paper presents the design and im-

plementation of a ROS2- based distributed control sys-

tem for an omnidirectional mobile robot using embed-

ded communication platforms and intelligent actuators.

The proposed architecture integrates ROS2, Micro-ROS,

and ESP32-based communication to enable real-time

interaction between software and hardware layers within

a modular distributed robotic framework. A structured

development methodology based on iterative SCRUM

phases was employed for system design, implementa-

tion, testing, and validation. Experimental results demon-

strate high functional reliability, robust closed-loop mo-

tion execution, and strong user acceptance, achieving a

98% satisfaction rate in perceived usability and func-

tionality. The proposed architecture provides a scalable

and cost-eﬀective solution for omnidirectional robotic

platforms in industrial and academic applications, con-

tributing to the advancement of distributed ROS2- based

mobile robotic systems.

Keywords ROS2 · micro-ROS · robotic control ·

intelligent actuators · ESP32 · distributed systems

1 Introduction

Mobile robotics has experienced signiﬁcant growth in

recent years, driven by advances in artiﬁcial intelligence,

embedded systems, and real-time communication tech-

nologies. Among the various mobile robotic platforms,

omnidirectional mobile robots (OMRs) have attracted

Jonathan S. Toapanta

Universidad Tecnol´ogica Indoam´erica, Ecuador.

E-mail: toapantajonathaning@gmail.com

Carlos Julio Fierro-Silva

Universidad Tecnol´ogica Indoam´erica, Ecuador.

E-mail: cﬁerro@indoamerica.edu.ec

increasing attention due to their superior maneuverabil-

ity, holonomic motion capabilities, and suitability for

complex environments such as industrial automation,

logistics, and service robotics [1,2,3,4]. Their ability to

move in arbitrary planar directions without changing

orientation makes them particularly advantageous in

dynamic and spatially constrained scenarios.

Despite these advantages, achieving accurate and

robust motion control in OMRs remains a challeng-

ing task due to nonlinear dynamics, model uncertain-

ties, external disturbances, and actuator coupling ef-

fects. Various advanced control strategies have been

proposed to address these issues, including sliding mode

control, adaptive control, and model predictive con-

trol [5]. Robust tracking controllers based on sliding

mode formulations have demonstrated improved dis-

turbance rejection and trajectory stability in omnidi-

rectional robotic systems [6,7]. More recently, neural-

network-based disturbance compensation and predic-

tive control approaches have further enhanced trajec-

tory tracking performance under uncertain operating

conditions [8,9,10].

Beyond control design, accurate modeling and cali-

bration remain essential to ensure reliable robot opera-

tion, particularly in systems aﬀected by mechanical tol-

erances, wheel slippage, and sensor noise. Prior studies

have investigated calibration and kinematic parameter

identiﬁcation methods to improve motion accuracy and

localization precision in omnidirectional robotic plat-

forms [11,12]. Additionally, eﬃcient navigation in real-

world environments requires the integration of global

and local planning strategies for path tracking and ob-

stacle avoidance, as demonstrated in several autonomous

navigation frameworks for mobile robots [13,14,15,16].

In parallel with these algorithmic advances, the Robot

Operating System 2 (ROS2) has emerged as a key mid-

dleware for modern robotic systems, enabling modu-

lar, scalable, and distributed real-time communication

through the Data Distribution Service (DDS) proto-

col [17,18]. Recent developments in ROS2 have signif-

icantly expanded the mobile robotics ecosystem, par-

30 International Journal of Engineering Insights, (2025) 3:1

ticularly through frameworks such as Nav2, which in-

tegrate localization, planning, and perception into uni-

ﬁed navigation architectures [19]. Furthermore, ROS2-

compatible simulation and deployment tools have fa-

cilitated the transition from algorithm development to

real-world robotic implementation [20], while distributed

robotic architectures have demonstrated improved scal-

ability and interoperability in modern robotic ecosys-

tems [21].

Despite these advances, implementing ROS2-based

distributed control systems in real-time robotic plat-

forms remains challenging. Communication delays, mid-

dleware overhead, and synchronization constraints can

signiﬁcantly aﬀect control responsiveness and system

reliability in embedded robotic applications. Recent stud-

ies have highlighted the importance of tracing and per-

formance analysis frameworks for evaluating ROS2 ex-

ecution behavior and communication latency in dis-

tributed systems [22,23].

To address embedded hardware limitations, Micro-

ROS has emerged as an extension of ROS2 for microcontroller-

based systems, enabling lightweight ROS2-compatible

communication in resource-constrained devices [24]. Al-

though previous studies have demonstrated the feasibil-

ity of ROS2 and Micro-ROS in distributed robotic ap-

plications, limited work has experimentally validated

their integration in omnidirectional mobile robot plat-

forms operating under real-world conditions.

Motivated by this gap, this paper presents the de-

sign and experimental validation of a ROS2-based dis-

tributed control architecture for an omnidirectional mo-

bile robot integrating Micro-ROS, ESP32 embedded con-

trollers, and intelligent actuator communication. The

proposed system enables modular interaction between

high-level ROS2 control nodes and low-level embedded

hardware within a distributed robotic framework. Ex-

perimental results demonstrate reliable functional per-

formance and strong user acceptance, supporting the

feasibility of ROS2-based distributed architectures for

omnidirectional mobile robotic systems.

2 Materials and Methods

2.1 System Architecture

The proposed system is based on a distributed robotic

architecture designed to enable real-time communica-

tion and control between high-level software compo-

nents and low-level embedded devices. The architec-

ture follows a layered approach integrating sensing, pro-

cessing, communication, and actuation modules within

a ROS2-based ecosystem. Fig. 1 illustrates the gen-

eral architecture of the proposed system, highlighting

Table 1 Hardware components of the proposed system

Component Description

ESP32 Embedded microcontroller for real-

time control and communication

Dynamixel Intelligent servomotors with inte-

grated sensing

OpenCM Dedicated controller for actuator

management

Robotic platform Omnidirectional mobile robot struc-

ture

the interaction between the user interface, ROS2 con-

trol nodes, DDS middleware, embedded communication

layer, and robotic actuators.

At the core of the system, ROS2 nodes are respon-

sible for control logic, data processing, and system co-

ordination. Communication between nodes is handled

through the Data Distribution Service (DDS), which

provides a scalable publish–subscribe mechanism suit-

able for real-time robotic systems.

To extend ROS2 capabilities to embedded platforms,

Micro-ROS is integrated as a lightweight communica-

tion layer. This enables seamless interaction between

the ROS2 network and resource-constrained devices.

The embedded layer is implemented using an ESP32

microcontroller, which executes low-level control rou-

tines and interfaces directly with the actuators.

The actuation system is composed of Dynamixel in-

telligent servomotors, which provide position, velocity,

and torque control along with real-time feedback. A

user interface module allows command input and mon-

itoring, enabling human interaction with the robotic

system.

This architecture ensures modularity, scalability, and

low-latency communication, which are essential for real-

time robotic applications.

2.2 Hardware Components

The hardware platform consists of embedded process-

ing units, actuator modules, and the robotic structure.

Table 1 summarizes the main components.

The ESP32 acts as an interface between ROS2 and

the physical hardware, executing control commands and

handling actuator communication. The Dynamixel ac-

tuators are selected due to their precision, reliability,

and compatibility with embedded control systems.

2.3 Software Framework

The software architecture is built upon ROS2, which

provides distributed communication, modularity, and

31 International Journal of Engineering Insights, (2025) 3:1

Omnidirectional

robot

Local Program

System on chip

Controller and

Actuator

Middleware

Micro-ROS Client

Publishers and

Development

Enviroment

User Interface

Fig. 1 General architecture of the ROS2-based distributed control system, showing the interaction between user interface,

middleware (DDS), embedded devices, and robotic actuators.

Table 2 Software components

Software Function

ROS2 Distributed middleware and commu-

nication framework

Micro-ROS Embedded communication layer

Python User interface and control scripts

Ubuntu 22.04 Operating system

scalability. The main software components are listed in

Table 2.

ROS2 nodes manage trajectory generation, control

execution, and data processing. Micro-ROS bridges com-

munication between ROS2 and the ESP32, enabling ef-

ﬁcient message exchange using lightweight protocols.

2.4 Kinematic and Control Model

The omnidirectional mobile robot is modeled using a

holonomic kinematic framework, which allows indepen-

dent motion along the planar axes and rotational move-

ment. The robot state in the global reference frame is

deﬁned as:

v =





˙x

˙y





(1)

where ˙x and ˙y represent the linear velocities in the

Cartesian plane, and

θ denotes the angular velocity.

For a four-wheeled omnidirectional robot equipped

with mecanum wheels, the relationship between the robot

velocity and the wheel angular velocities is given by:

ω = J v (2)

where ω = [ω

]

is the vector of wheel

angular velocities, and J is the Jacobian matrix deﬁned

as:

J =







1 −1 −(L + W )

1 1 (L + W )

1 1 −(L + W )

1 −1 (L + W )







(3)

where r is the wheel radius, and L and W represent

the distances from the robot center to the wheel along

the longitudinal and lateral axes, respectively.

The desired trajectory is deﬁned as v

= [ ˙x

˙y

]

The tracking error is computed as:

e =





− x

− y

− θ





(4)

A proportional control law is adopted to regulate

the tracking error:

v = K

e (5)

where K

= diag(k

, k

) is a diagonal gain ma-

trix.

The computed velocity commands are transformed

into wheel velocities using the kinematic model and

transmitted through the ROS2 communication layer to

the embedded controller.

This control strategy is selected due to its low com-

putational complexity, making it suitable for real-time

implementation in resource-constrained embedded plat-

forms such as the ESP32, while still providing stable

trajectory tracking performance.

2.5 Closed-Loop Control Scheme

The control architecture follows a closed-loop scheme,

as illustrated in Fig. 2. The desired trajectory is gener-

ated at the ROS2 level and compared with the actual

robot state to compute the tracking error.

32 International Journal of Engineering Insights, (2025) 3:1

Reference (xd, yd, ?d)

ROS2 Controller

DDS Middleware (Pub/Sub)

Micro-ROS Client / ESP32

ESP32 Controller

Dinamixel Motors

Omnidirectional Robot

Sensors

Fig. 2 Closed-loop control scheme of the ROS2-based omni-

directional robotic system.

Control commands are transmitted via DDS to the

embedded system using Micro-ROS. The ESP32 exe-

cutes actuator commands, while feedback from sensors

is sent back to ROS2 nodes. This feedback loop enables

real-time correction and ensures system stability.

2.6 System Workﬂow

The operational workﬂow of the proposed system fol-

lows a sequential process that ensures real-time interac-

tion between the high-level control layer and the embed-

ded system. The process begins with the initialization

of ROS2 nodes and the conﬁguration of communication

interfaces.

Once the system is initialized, the DDS communi-

cation layer is established, enabling data exchange be-

tween ROS2 nodes and the embedded platform through

Micro-ROS. After successful connection, the system waits

for user commands, which are processed at the ROS2

level to generate appropriate control signals.

These control commands are transmitted through

the DDS middleware to the embedded system, where

the ESP32 executes the corresponding actuator control

actions. The Dynamixel actuators perform the required

motion, while sensor data is continuously acquired.

Feedback information is then transmitted back to

the ROS2 nodes, allowing real-time monitoring and up-

Table 3 Functional requirements validation

ID Requirement Attempts Success Status

RF1 Connection 40 38 Completed

RF2 Linear ve-

locity

40 36 Completed

RF3 Angular ve-

locity

40 36 Completed

RF4 Stop 40 40 Completed

RF5 Exit 40 40 Completed

dating of the system state. This continuous exchange of

information ensures closed-loop operation and enables

dynamic response to changes in system conditions.

2.7 Development Methodology

The system was developed using an iterative methodol-

ogy inspired by the SCRUM framework, including plan-

ning, implementation, testing, and validation phases.

Each iteration focused on integrating speciﬁc modules

such as communication, control, and hardware interac-

tion.

This approach allowed progressive system reﬁnement

and facilitated the identiﬁcation of performance bot-

tlenecks, particularly those related to communication

latency and real-time execution.

3 Results

3.1 Functional Validation

The functional performance of the proposed system was

evaluated through a series of execution tests, as sum-

marized in Table 3. The table presents the number of

attempts and successful executions for each functional

requirement.

Figure 3 illustrates the experimental setup used dur-

ing functional validation of the proposed control archi-

tecture. The interface provides real-time command ex-

ecution for omnidirectional motion control, while the

terminal output conﬁrms successful ROS2-based com-

munication and command processing during operation.

As shown in Table 3, the system achieved high re-

liability across all evaluated functions. The connection

module reached a success rate of 95%, while motion

control functions such as linear and angular velocity

achieved 90%. The stop and exit functions demonstrated

complete reliability with 100% success rates.

33 International Journal of Engineering Insights, (2025) 3:1

Fig. 3 Experimental functional validation setup of the proposed ROS2-based omnidirectional mobile robot control system.

The ﬁgure shows the graphical user interface used for command execution, the physical omnidirectional robot platform, and

the ROS2 terminal feedback during real-time operation.

Table 4 Likert scale used for TAM-based evaluation

Score Description Interpretation

1 Strongly disagree Very negative

2 Disagree Negative

3 Slightly disagree Slightly negative

4 Neutral Neutral

5 Slightly agree Slightly positive

6 Agree Positive

7 Strongly agree Very positive

3.2 User Acceptance Evaluation

A user acceptance evaluation was conducted based on

the Technology Acceptance Model (TAM), using a struc-

tured questionnaire. The evaluation was performed by

a laboratory engineer, providing qualitative validation

of system usability.

The rating scale used in the evaluation is presented

in Table 4, ranging from 1 (strongly disagree) to 7

(strongly agree).

The results related to perceived usefulness are shown

in Fig. 4. The participant assigned the highest rating

(7) to most questions, indicating strong agreement re-

garding the usefulness of the developed system.

Similarly, Fig. 5 presents the results for perceived

ease of use. The responses show consistently high scores,

conﬁrming that the system is easy to operate and meets

user expectations.

4 Discussion

The experimental results demonstrate that the proposed

ROS2-based distributed control architecture provides

reliable real-time performance for omnidirectional mo-

Fig. 4 Perceived usefulness evaluation results

Fig. 5 Perceived ease of use evaluation results

bile robot operation. Functional validation showed suc-

cess rates above 90% for all evaluated control tasks,

with critical commands such as emergency stop and

shutdown achieving 100% reliability. These ﬁndings con-

ﬁrm that the proposed architecture is suitable for prac-

tical mobile robotic applications requiring robust low-

latency communication.

34 International Journal of Engineering Insights, (2025) 3:1

The integration of ROS2 with Micro-ROS and ESP32

embedded controllers proved eﬀective for enabling dis-

tributed communication between high-level motion plan-

ning modules and low-level actuator control. This result

is consistent with previous studies reporting that ROS2

signiﬁcantly improves modularity, scalability, and real-

time interoperability in distributed robotic systems com-

pared with ROS1-based implementations [19,24,22].

Regarding omnidirectional robotic platforms, the achieved

motion reliability aligns with prior works highlighting

the importance of accurate distributed control archi-

tectures for maintaining stable trajectory tracking and

motion precision in holonomic robots [6,9,11]. The pro-

posed architecture complements these control-oriented

studies by providing a practical communication and em-

bedded implementation framework capable of support-

ing real-time control strategies in physical omnidirec-

tional platforms.

The TAM-based user evaluation yielded an over-

all acceptance rate of 98%, indicating that the devel-

oped platform is perceived as highly usable and eﬀec-

tive. Similar high user acceptance has been reported in

robotics interface studies where intuitive ROS2-based

control environments improved operator interaction and

deployment eﬃciency in laboratory and industrial set-

tings.

Nevertheless, some limitations remain. The user ac-

ceptance evaluation was conducted with a limited sam-

ple size, which restricts the statistical generalizability

of the usability results. Furthermore, although the func-

tional performance was satisfactory, future studies should

incorporate quantitative latency benchmarking and end-

to-end timing analysis, as communication delays remain

a critical factor in distributed ROS2 robotic systems

[22,23].

Overall, the proposed architecture demonstrates that

combining ROS2, Micro-ROS, and embedded controllers

constitutes an eﬀective framework for omnidirectional

mobile robot control, oﬀering a scalable and modular

solution for next-generation distributed robotic plat-

forms.

5 Conclusions

This paper presented the design and implementation of

a distributed control system for an omnidirectional mo-

bile robot based on ROS2 and embedded communica-

tion technologies. The proposed architecture integrates

ROS2, Micro-ROS, and ESP32-based control, enabling

eﬃcient interaction between high-level software compo-

nents and low-level hardware devices.

The experimental results demonstrated that the sys-

tem achieves high reliability in functional execution,

with success rates above 90% across all evaluated re-

quirements. Critical control operations reached full re-

liability, conﬁrming the robustness of the implemented

architecture. Furthermore, the user acceptance evalua-

tion based on the TAM model showed a high level of

satisfaction, with an overall acceptance rate of 98%, in-

dicating that the system is both eﬀective and easy to

use.

The proposed approach oﬀers signiﬁcant advantages

in terms of modularity, scalability, and real-time com-

munication, making it suitable for applications in in-

dustrial automation and academic research. The inte-

gration of embedded systems with ROS2 expands the

capabilities of distributed robotic systems, particularly

in resource-constrained environments.

Future work will focus on extending the system to

multi-robot scenarios, incorporating advanced control

strategies, and performing detailed latency and per-

formance analysis under diﬀerent operating conditions.

Additionally, further user studies will be conducted to

validate the system with a larger and more diverse group

of participants.

Conﬂict of interest

The authors declare no conﬂict of interest.

References

1. H. Taheri and C. X. Zhao, “Omnidirectional mobile

robots, mechanisms and navigation approaches,” Mech-

anism and Machine Theory, vol. 153, p. 103958, 2020.

2. K. Kanjanawanishkul, “Omnidirectional wheeled mobile

robots: Wheel types and practical applications,” Inter-

national Journal of Advanced Mechatronic Systems, vol. 6,

no. 6, pp. 289–302, 2015.

3. G. Campion, G. Bastin, and B. D’Andrea-Novel, “Struc-

tural properties and classiﬁcation of kinematic and dy-

namic models of wheeled mobile robots,” IEEE Transac-

tions on Robotics and Automation, vol. 12, no. 1, pp. 47–62,

1996.

4. P. Muir and C. Neuman, “Kinematic modeling of wheeled

mobile robots,” Journal of Robotic Systems, vol. 4, no. 2,

pp. 281–340, 1987.

5. M. Zhu, D. Gong, Y. Zhao, J. Chen, and J. Qi, “Com-

pliant force control for robots: A survey,” Mathematics,

vol. 13, no. 13, p. 2204, 2025.

6. A. Azzabi and K. Nouri, “Design of a robust tracking con-

troller for a nonholonomic mobile robot based on sliding

mode,” International Journal of Advanced Robotic Systems,

2021.

7. P. Q. Baban and M. E. Ahangari, “Adaptive terminal

sliding mode control of a non-holonomic wheeled mobile

robot,” International Journal of Vehicle Information and

Communication Systems, vol. 9, no. 3, pp. 1–15, 2024.

8. C.-G. e. a. Yun, “Trajectory tracking control using rbf

neural network,” Journal of the Brazilian Society of Me-

chanical Sciences and Engineering, 2023.

35 International Journal of Engineering Insights, (2025) 3:1

9. T. e. a. Ding, “Trajectory tracking using mpc velocity

control,” Mechatronics, 2022.

10. C. e. a. Ren, “Conditional disturbance negation control,”

IEEE Robotics and Automation Letters, 2022.

11. A. T. e. a. Popovici, “Kinematics calibration of omnidi-

rectional robot,” Sensors, 2022.

12. B. e. a. Siciliano, Robotics: Modelling, Planning and Con-

trol. Springer, 2010.

13. S. e. a. Sahloul, “Hybrid navigation methods,” Journal of

Robotics and Control, 2021.

14. S. e. a. Thrun, Probabilistic Robotics. MIT Press, 2005.

15. F. Ahuraka, P. McNamee, Q. Wang, Z. N. Ahmadabadi,

and J. Hudack, “Chaotic motion planning for mobile

robots: Progress, challenges, and opportunities,” IEEE

Access, vol. 11, pp. 134 000–134 020, 2023.

16. Y. Shen, Y. Shen, Y. Zhang, C. Huo, and Z. Shen, “Re-

search progress on path planning and tracking control

methods for orchard mobile robots in complex scenar-

ios,” Agriculture, vol. 15, no. 18, p. 1917, 2025.

17. M. e. a. Quigley, “Ros: an open-source robot operating

system,” ICRA Workshop, 2009.

18. G. Pardo-Castellote, “Dds overview,” in IEEE ICDCSW,

2003.

19. S. e. a. Macenski, “Survey of mobile robotics algorithms

in ros2,” Robotics and Autonomous Systems, 2023.

20. M. Z. e. a. Andreasen, “Maes simulator for ros2,” Artiﬁ-

cial Life and Robotics, 2023.

21. F. C. Reﬁs, N. A. Mahammedi, C. A. Kerrache, and

S. Dhelim, “From network sensors to intelligent systems:

A decade-long review of swarm robotics technologies,”

Sensors, vol. 25, no. 19, p. 6115, 2025.

22. C. B´edard et al., “ros2 tracing framework,” IEEE Robotics

and Automation Letters, 2022.

23. Y. e. a. Maruyama, “Exploring the performance of ros2,”

in RTCSA, 2016.

24. S. e. a. Garrido-Jurado, “Micro-ros for microcontrollers,”

IEEE Robotics and Automation Magazine, 2021.

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