Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 164-176
https://doi.org/10.36561/ING.30.11
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
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164
System-Level Design and Outdoor Validation of a Solar-Powered Mobile
Robot for Autonomous Environmental Monitoring
Diseño a Nivel de Sistema y Validación en Exteriores de un Robot Móvil Solar para
la Monitorización Ambiental Autónoma
Projeto ao Nível do Sistema e Validação Externa de um Robô Móvel Alimentado a
Energia Solar para Monitorização Ambiental Autónoma
Halar Mustafa
1
, Sadiq Ur Rehman
2
(*), Muhammad Ahsan Shaikh
3
Recibido: 27/01/2026 Aceptado: 30/03/2026
Summary. - The need to explore and use mobile robots outside is rising, mainly in terms of monitoring the environment
and conducting inspections outside grid resources, where energy autonomy, terrain variability, and communication are
essential. The purpose of this paper is to propose an innovative solar-powered mobile robot that integrates solar energy
harvesting, four-wheel drive, two channels of wireless communication, and passive thermal management. The majority
of previous works emphasized and focused on single components and experimental investigations, while this paper
instead focuses on experience with practical implementations of these technologies outside, thereby gaining experience
with similar robots outside, leading to observations on energy efficiency, movement, control, and communication, with
possible implications for improving the next generation of robots outside. The experiment outside using various terrain
types of grass, gravel, and earth successfully proved the positive energy balance of the robot, easy energy-efficient
motion, good wireless control, and good wireless video streaming with little delay.
Keywords: Solar-powered robots, Terrain-adaptive locomotion, Energy harvesting, Autonomous mobile robots, Off-
grid monitoring.
(*) Corresponding author.
1
Master of Engineering, Faculty of Engineering Sciences & Technology, Hamdard University (Pakistan), halar.mustafa@hamdard.edu.pk,
ORCID iD: https://orcid.org/0000-0002-7021-5010
2
PhD, Associate Professor, Faculty of Engineering Science and Technology, Iqra University (Pakistan), sadiq.rehman@iqra.edu.pk.
ORCID iD: https://orcid.org/0000-0002-6308-450X
3
PhD, Lecturer, Faculty of Engineering Sciences & Technology, Hamdard University (Pakistan), muhammad.ahsan@hamdard.edu.pk,
ORCID iD: https://orcid.org/0000-0003-2408-5689
H. Mustafa, S. Ur Rehman, M. Ahsan Shaikh
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 164-176
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Resumen. - La necesidad de explorar y utilizar robots móviles en exteriores es cada vez mayor, principalmente para
la monitorización del entorno y la realización de inspecciones fuera de la red eléctrica, donde la autonomía energética,
la variabilidad del terreno y la comunicación son esenciales. El objetivo de este trabajo es proponer un innovador
robot móvil solar que integra la captación de energía solar, tracción en las cuatro ruedas, dos canales de comunicación
inalámbrica y gestión térmica pasiva. La mayoría de los trabajos previos se centraban en componentes individuales e
investigaciones experimentales, mientras que este trabajo se centra en la experiencia con implementaciones prácticas
de estas tecnologías en exteriores, obteniendo así experiencia con robots similares en exteriores, lo que ha llevado a
observaciones sobre eficiencia energética, movimiento, control y comunicación, con posibles implicaciones para la
mejora de la próxima generación de robots en exteriores. El experimento en exteriores, utilizando diversos tipos de
terreno como césped, grava y tierra, demostró con éxito el balance energético positivo del robot, su fácil movimiento
con eficiencia energética, su buen control inalámbrico y su buena transmisión de vídeo inalámbrica con poca latencia.
Palabras clave: Robots alimentados con energía solar, Locomoción adaptativa al terreno, Recolección de energía,
Robots móviles autónomos, Monitoreo fuera de la red.
Resumo. - A necessidade de explorar e utilizar robôs móveis em ambientes exteriores é crescente, principalmente para
monitorizar o ambiente e realizar inspeções em locais sem acesso à rede elétrica, onde a autonomia energética, a
variabilidade do terreno e a comunicação são essenciais. O objetivo deste artigo é propor um robô móvel inovador
alimentado a energia solar que integra a captação de energia solar, tração às quatro rodas, dois canais de
comunicação sem fios e gestão térmica passiva. A maioria dos trabalhos anteriores enfatizou e focou-se em
componentes individuais e investigações experimentais, enquanto este artigo se centra na experiência com
implementações práticas destas tecnologias em ambientes exteriores, adquirindo experiência com robôs semelhantes
em ambientes exteriores, o que leva a observações sobre eficiência energética, movimento, controlo e comunicação,
com possíveis implicações para a melhoria da próxima geração de robôs para uso exterior. A experiência externa,
utilizando vários tipos de terreno (relva, cascalho e terra), comprovou com sucesso o balanço energético positivo do
robô, a facilidade de movimento com eficiência energética, o bom controlo sem fios e a boa transmissão de vídeo sem
fios com baixa latência.
Palavras-chave: Robôs movidos a energia solar, Locomoção adaptativa ao terreno, Captação de energia, Robôs
móveis autónomos, Monitorização fora da rede elétrica.
H. Mustafa, S. Ur Rehman, M. Ahsan Shaikh
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1. Introduction. - Outdoor mobile robots are being used in environmental monitoring, precision agriculture,
surveillance, and inspecting infrastructural facilities, among others [1]. Such robots should be able to function well in
unstructured, or off-grid environments characterized by energy autonomy, terrain flexibility, and effective
communication. Typically, battery-powered robots have short missions and require quite regular servicing, particularly
when they are required to operate in remote areas [2].
Solar energy harvesting has an excellent potential as a method for extending operational endurance while minimizing
human intervention. However, the integration of photovoltaic (PV) systems in mobile platforms involves issues related
to variable energy supply, charging efficiency, and real-time energy management [3]. Wheeled robots remain
mechanically simple and energy-efficient but often suffer from performance degradation on soft or uneven terrain
owing to slippage and increased power demands [4-5].
Previous research in the field of solar-powered robots has involved investigations into PV modules, energy storage,
power electronics, suspension, and communication technologies. However, most studies are confined either to single
subsystems or controlled laboratory conditions, or indeed specialized applications, thus providing very limited insight
into system-level integration in real-life conditions. Energy harvesting, terrain-adaptive locomotion, wireless
communication, and thermal performance experimental validation of a completely functional robot has barely been
investigated.
This paper will address these gaps through the system-level design and outdoor evaluation of a solar-powered, terrain-
adaptive mobile robot for off-grid monitoring. The approach emphasizes practical integration over component-level
innovation through a holistic platform that combines photovoltaic energy harvesting, battery storage, four-wheel-drive
locomotion, dual-channel wireless communication, and passive thermal management. Experimental validation across
multiple terrains is presented; interaction between energy generation, terrain-dependent mobility, communication
performance, and thermal stability is quantified.
The contributions of this work are highlighted as follows:
• A modular system-level architecture integrating energy harvesting, locomotion, communication, and thermal
management.
• Outdoor experiments on grass, gravel, and dirt with evaluation of energy harvesting and consumption,
mobility, communication latency, and thermal behaviour.
• Practical insights on trade-offs between energy autonomy, terrain adaptability, and communication reliability
for off-grid monitoring robots.
2. Literature Review. - Outdoor-deployed mobile robots face challenges in terms of energy autonomy, terrain
adaptability, and wireless communication. The lack of grid power supply in an outdoor environment has encouraged
researchers to explore alternative sources of energy, such as renewable energy sources, especially PV systems, for
efficient energy autonomy with low human intervention. Even though various reviews have been conducted on the
integration of PV systems and energy efficiency, they have been more theoretical and have limited practical
applications.
Practical applications of solar-powered robotics include agricultural field robots, transport robots, and experimental
robots. Solar-powered surveillance robots have been found to have the potential to charge the battery while moving on
any terrain in real field conditions [6]. General-purpose robots used in agricultural applications use solar power to
perform several operations, including irrigation, sowing, and crop monitoring [7]. Even simple robots have been used
to validate the practical applications of solar-powered robots [8-10]. Moreover, solar-powered robots have also been
used for marine and industrial monitoring, which can ensure very long duration and autonomous operations [11].
Despite all these advancements, energy harvesting along with terrain-dependent mobility, wireless communication,
and thermal behavior, in a unified and integrated manner, has rarely been addressed in existing platforms. In most
research, carried out either in a laboratory environment or in applications where the scope of generalization is limited,
subsystems have been addressed in an isolated manner. Quantitative analysis of energy balance considering load
variation due to terrain has rarely been addressed. The aim of this paper is to bridge this gap by proposing a unified
system design, keeping in view deployability, energy awareness, and experimentation.
H. Mustafa, S. Ur Rehman, M. Ahsan Shaikh
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Ref
Platform /
Robot
Application
Solar
System
Mobility /
Locomotion
Key Focus /
Findings
Limitations
[6]
Surveillance
Robot
Agricultural field
monitoring
PV panels
+ battery
4WD, all-terrain
Demonstrated
feasibility of solar-
charging for multi-
day outdoor
operation
Limited terrain
types, short-term
field test
[7]
Review of PV
systems
General robotics /
energy harvesting
PV-based
energy
harvesting
Various
Comprehensive
overview of PV
integration and
energy management
Mostly
theoretical,
limited
experimental
robotics data
[8]
Multipurpose
agricultural
robot
Irrigation,
seeding, crop
monitoring
PV +
battery
Wheeled,
Bluetooth/Android
control
Real-world
integration with IoT
& solar harvesting
Short-term testing,
limited terrain
adaptability
[9]
RC
autonomous
robot
Educational /
experimental
PV +
battery
Wheeled
Demonstrated low-
cost solar-powered
operation
No long-duration
field validation
[10]
Smart farming
robot
Multi-purpose
precision
agriculture
PV +
battery
Wheeled
Integration with IoT
and computer vision
for energy-aware
operation
Tested in
controlled field
plots; scalability
unclear
[11]
RaccoonBot
Environmental
monitoring
PV panels
+ tracking
Wire-traversing
robot
Autonomous solar
tracking, persistent
monitoring
Complex setup,
high-cost, not
general-purpose
Table I: Summary of Relevant Research
3. System Architecture. - In this section, the system-level architecture of the proposed solar-powered mobile robot
designed to operate in territories varying in topology is introduced. The architectures incorporate integration in
renewable energy harvesting, power, mobility, and wireless communication. In contrast to architectures designed to
optimize each functional component, this proposed architecture is designed to work together in real-world conditions,
where renewable energy availability, topographic variation, and wireless communication are interrelated.
Figure I shows how the energy subsystem, control and communication subsystem, and locomotion subsystem are
interlinked for this system. Table II show the hardware specification of the prototype
Subsystem
Parameter
Specification / Value
PV Panel
Rated Power
50 W
Area
0.35 m²
Mounting Angle
30° tilt adjustable
Controller
MPPT Solar Charge Controller,
12 V
Battery
Chemistry
LiFePO4
Nominal Voltage
12 V
Capacity
12 Ah
Maximum Continuous Discharge
10 A
Internal Resistance
50 mΩ
C-Rate
1 C
Mobility / Drive
Robot Mass
7 kg (including PV, battery,
payload)
Wheel Diameter
0.15 m
Ground Clearance
0.08 m
Gear Ratio
1:20
Motors
4 × DC brushed, 24 W nominal,
12 V
ESC
4 × 12 V, 15 A
Max Torque per Motor
0.8 Nm
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Sensors / Navigation
Wheel Encoders
500 CPR optical encoders
IMU
9-axis MEMS (gyro, accel,
magnetometer)
GPS
1 Hz, horizontal accuracy ±2 m
Thermal Monitoring
Contact Thermometers
3 × PT100 sensors on motors and
ESC
Sampling Frequency
1 Hz
Communications
Bluetooth Module
HC-05, 9600 baud, SPP protocol
Video Streaming
720p, 5 Mbps, Wi-Fi 2.4 GHz
Payload / Other
Maximum Payload
2 kg
Total System Dimensions
0.45 m × 0.35 m × 0.25 m
Table II: Hardware Specifications
3.1 System Configuration. - The robotic platform is divided into three strongly integrated systems:
• Renewable energy and power management subsystem
• Control and communication subsystem
• Locomotion and Mechanical Subsystem
These systems run simultaneously for the purpose of constant monitoring of the outdoors. Solar power is harnessed
for storing in the onboard battery bank, which in turn provides controlled power for the control electronics, motor
drivers, and peripheral sensing devices. The Arduino Mega 2560 [12] acts as the main controlling system for the
platform, controlling the execution of motion, monitoring, and safe handling of the system. The wireless
communication module allows for low-latency command & feedback transmissions.
3.2 Renewable Energy and Power Management Subsystem. - The basis of the control system is an Arduino Mega
2560 microcontroller, which was selected because of more than one serial interface and the possibility of real-time
control. This microcontroller executes all the motion instructions, detects the level of battery voltages, sets the
conditions for safety, and interacts with all the other peripheral devices.
A dual wireless communication system is used to segregate the motion control and video streaming. The Bluetooth
device HC-05 [13] is used to transmit the control commands through a smartphone-based application with lower
latency, and a Wi-Fi IP camera is used for streaming videos. This approach allows for independence in motion control
and video streaming; hence the system is less prone to interference. Such relay driver circuits are included using
2N2222 transistors [14] to connect the Arduino lower outputs to motor control paths at higher currents, thereby saving
the microcontroller from undergoing motor-damaging currents.
Figure I. System Block Diagram
3.3 Locomotion and Mechanical Subsystem. - The locomotion system comprises four DC gear motors with high
torque. The four motors can move through a four-wheel drive mechanism. Each motor is coupled to an electronic speed
controller. In this way, a constant torque is created. The four-wheel drive feature allows the robot to move effectively
on grass, gravel, or untrenched soil.
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The mechanical design consists of a modular PVC chassis, whose material of choice was determined by its ability to
be corrosion-resistant, an electrical insulator, easy to work with, and lightweight at the same time. The solar panels,
battery bank, motors, and control units are mounted inside the chassis, whose design makes its center of gravity very
low to ensure stability of the entire unit while moving on slopes.
Figure II. Assembled Prototype
3.4 System Integration and Operational Flow. - Solar energy, in the operation stage, is continuously being tapped
and supplied either directly to the load or stored within the battery bank, depending on the availability and demand. In
the start-up stage, the Arduino initialises all the peripherals, checks the voltage levels at the batteries, and sets up
wireless communication. The motion signals commanded from Bluetooth get converted into pulse-width modulation
(PWM) signals to command electronic stability control (ESCs) and motors.
While doing so, it also has the ability to do live video transmission via Wi-Fi connectivity. Along with this, the
situational awareness for navigation is also facilitated via this feature. The safety feature serves the purpose of constant
monitoring of the communications integrity as well as the level of voltage. If the critical threshold is achieved, the
system is switched to either safety mode or standby.
4. Methodology. - The methodological framework adopted in the present investigation is intended to experimentally
assess the system performance of the solar-powered mobile robot. The emphasis is on the system-level performance,
such as energy consumption, energy generation, terrain-based locomotion, communication, and thermal stability.
All experiments are performed outdoors to simulate the combined effects of environmental variability, terrain, and
solar energy availability. The experiments are performed under clear to partly cloudy conditions, with an ambient
temperature ranging from 18 °C to 32 °C and solar irradiance ranging from 550 W/m² to 950 W/m², as measured using
a handheld solar irradiance meter.
4.1 Energy Measurement and Mission Profile. - Energy autonomy is defined as the ability of the robot to maintain
operation using the harvested solar power, without the need to recharge the battery from an external source. Battery
voltage levels (between 11.9 and 12.6 V) are monitored as an indicator of SOC, but the results are based on the amount
of harvested vs. consumed energy in units of Wh rather than SOC.
A standardized mission cycle was defined:
1. Idle state, i.e., the controller and the camera are turned on (5 min)
2. Active locomotion, i.e., moving over the terrain (15 min)
3. Video streaming, i.e., sending data via Wi-Fi (15 min)
4. Solar-assisted charging, i.e., recharging phase (15 min)
The above mission cycle is repeated five times, considering the changing environment, and the results are averaged.
The results were obtained from calibrated sensors.
Energy measurements were obtained from calibrated sensors:
• PV input (Vpv, Ipv) – ±0.5 % accuracy, 1 Hz sampling
• Battery current (Ibat) – ±0.5 % accuracy, 1 Hz sampling
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Power was integrated over time to compute energy in Wh:
=
P() ∆t Eq (1)
where ∆t is the sampling interval. The uncertainty in the measurements is expressed as the average value ± standard
deviation over multiple measurements. To ensure physical consistency and auditability in the evaluation of the system's
performance, the energy generated and the energy consumed have been determined and expressed as energy in Watt-
hours (Wh) by calculating the time integral of the measured electrical signal.
The instantaneous power generated by the solar panels is calculated as:
() = () * () Eq (2)
where, () and () are the voltage and current generated by the solar panels, respectively. In the same way,
the power being consumed by the system is determined as:
() = () * () Eq (3)
The net energy balance in the system is determined as:
= – Eq (4)
The state of charge of the battery, i.e., battery SOC, has been estimated from voltage measurements within the range
of 11.9 V to 12.6 V. Yet, since the SOC estimation based on voltage under load conditions depends on the internal
battery resistance, it is only used qualitatively rather than quantitatively in the evaluation of the battery.
The uncertainty in the measurements results from the accuracy of the sensors, as well as the environmental conditions,
but since the experiments are performed several times, the results are averaged.
4.2 Energy Storage Estimation. - The nominal estimation of the stored energy within the battery system was obtained
as follows:
= × Eq (5)
Here, is the battery voltage, and is the capacity rating, expressed in ampere-hours. This is the theoretical
calculation, but the practical evaluation is done based on the power flow during operation.
4.3 Locomotion Performance Evaluation. - The locomotion performance of the robot was tested on various terrains
such as grass, gravel, and dirt. The test was conducted on dry surfaces with a maximum slope of 15 degrees. Various
parameters of the locomotion performance of the robot are speed, wheel slip, torque, turn radius, and
vibration/acceleration level. To find the average linear velocity of the robot, a sensor fusion technique was used with
the help of wheel encoders, a 9-axis IMU, and GPS. The values are sampled at a frequency of 50 Hz. To reduce the
noise in the measurement values, a low-pass Butterworth filter with a cutoff frequency of 5 Hz was applied. To find
the speed of the robot, the values are calculated over a fixed distance and then averaged out of five iterations.
Wheel slip (%) of the robot was calculated by finding the percentage deviation between the expected distance
calculated by the wheel’s angular velocity and the actual distance traveled by the robot on the surface using the sensor
fusion technique of GPS and IMU. On the gravel surface, the wheel slip was found to be up to 12%, while on the grass
and dirt surfaces, the wheel slip was negligible.
Motor torque τ was calculated using:
= mech / Eq (6)
where Pmech is the mechanical power calculated from the input electrical power, corrected using an 85% drivetrain
efficiency estimate, and ω is the angular velocity measured using the encoder. The above equation gives an estimation
of the torque requirement over different types of terrain. The peak accelerations are measured using the onboard 9-axis
IMU, operating at 50 Hz, and filtered using a low-pass filter to remove noise. The peak levels of vibration, 2.5g, are
measured on uneven terrain, and the levels are high on gravel terrain.
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4.4 Wireless Communication Evaluation. - The performance of the wireless communication system was tested using
a dual-channel communication system, which included Bluetooth control and Wi-Fi video streaming.
For Bluetooth control, the latency (<100 ms) was tested using time-stamped control commands, which were transmitted
over ≥5 trials, achieving 10-15 m range, allowing near-real-time control. Video streaming was done using the V380
Pro App, transmitting 1080p, 25 fps, 5 Mbps, using Wi-Fi 2.4 GHz. The latency of the video ranged from 0.5 to 1.5
seconds, while the variations in the quality are due to signal strength, distance (30-50 m), and environment interference.
4.5 Thermal Performance Evaluation. - Temperature readings were obtained through the use of PT100 contact
thermometers, which are attached directly to the motor casing, ESC, and battery surface, ensuring good contact through
thermal paste. The sampling rate was 1 Hz, and the ambient environment was maintained at a range of 18-32°C. The
readings are recorded continuously throughout each experiment.
4.6 Terrain Characterization. - The experimental terrains were classified in order to study the effect of surface
properties on robot performance. Three terrain types were considered:
• Grass: moderately compliant surface with good traction and low rolling resistance.
• Gravel: loose particles with higher rolling resistance, higher wheel slip (up to 12%), and uneven surface
roughness.
• Dirt: compact soil with stable traction and minimal surface compliance.
All experiments were performed in dry conditions, and the slope was varied from 0° to 15° to assess the robot's
locomotion over inclined surfaces. The surface roughness and compliance are also noted qualitatively. The
environmental conditions, such as the ambient temperature (18–32 °C) and solar irradiance (550–950 W/m²), are also
recorded to assess the energy harvesting and locomotion performance. The classification of the terrains, along with the
slope, provides an repeatable method to assess the robot's performance, as indicated by parameters such as speed,
torque, and slip, as well as the vibration. Figure III shows the system flow diagram for the proposed system with the
experiments performed in the above cycles.
Figure III. System flow chart
5. Results. - Field experiments have been conducted to verify the capabilities of the robot in terms of its locomotion,
efficiency of energy consumption, thermal properties, wireless communication, and battery charging/discharging. The
experiments have been conducted on grass, gravel, earth, and rough terrain with slopes up to 15° in Karachi, Pakistan.
5.1 Energy Consumption& Thermal Characteristics. - The power consumption of the system was evaluated at three
modes of operation: idle mode, locomotion mode, and video streaming mode. From Fig IV and Table III, it can be
observed that the system consumed around 5 W of power during the idle mode of operation due to the controller and
the camera subsystems. During the locomotion mode on flat surfaces, the system consumed on an average around 34.5
W of power, which translates into an energy consumption of 11.5 Wh during a 20-minute interval. During the
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Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 164-176
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locomotion mode on gravel surfaces, the system consumed around 44.5 W of power due to the rolling resistance and
wheel slips of the system, which translates into an energy consumption of 11.1 Wh during a 15-minute interval. During
the video streaming mode of operation, the system consumed around 6.5 W of power, which translates into an energy
consumption of 3.3 Wh during a 30-minute interval.
The thermal measurement results, as shown in Fig. V, indicate that the DC motor was subjected to an average
temperature of 45 °C, with a maximum of 52 °C, while the critical temperature was 73 °C. The ESC was subjected to
an average temperature of 42 °C, with a maximum of 47 °C, while the limit was 65 °C. The battery was subjected to a
safe range, with an average, maximum, and limit values of 33 °C, 42 °C, and 55 °C, respectively. The placement of
the sensor, sampling rate, and ambient environment were monitored for each run.
Mode
Avg Power (W)
Energy Consumed (Wh)
Idle
5.0 ± 0.2
0.42 ± 0.02 (5 min)
Locomotion (Flat)
34.5 ± 1.1
11.5 ± 0.5 (20 min)
Locomotion (Gravel)
44.5 ± 1.3
11.1 ± 0.6 (15 min)
Video Streaming
6.5 ± 0.3
3.3 ± 0.1 (30 min)
Table III. Energy comsumption of operational modes
Figure IV. Energy consumption breakdown by operation mode
Figure V. Temperature Variation of Components
5.2 Terrain-Dependent Locomotion. - The four-wheel drive robot was able to traverse different types of terrain. The
PVC provided the required shock absorption, hence stability on rough terrain. It was also able to climb a 15° slope.
Very harsh terrain, such as heavy mud or large rocks, would be a problem, depending on the size of the wheels and
ground clearance. The average speeds obtained were 1.0 m/s on gravel, 1.4 m/s on dirt soil, with a turn radius of 0.5
m and a wheel slip of 12% on gravel. The peak acceleration on the rough terrain was 2.5 g. Table IV represents the
locomotion performance metrics used.
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Metric
Grass
Gravel
Dirt
Avg Speed (m/s)
1.2
1
1.4
Turning Radius (m)
0.5
0.5
0.5
Wheel Slip (%)
3
12
2
Torque (Nm)
0.7
0.9
0.8
Peak Vibration (g)
2
2.5
1.8
Table IV. Locomotion Performance Metrics
It can be seen from Fig VI that gravel terrain causes higher torque, slippage, and hence reduced speeds and inclines,
as opposed to grass and dirt terrain that offer better gripping properties and reduced mechanical losses.
Fig VI. Comparison between the traction and performance parameters on grass, gravel, and dirt track.
5.3 Wireless Communication Performance. - Control via Bluetooth and video transmission through the V380 Pro
App (See Fig VII) also performed well. The delay between control commands was less than 100 ms, ensuring near-
real-time control, but the delay in video transmission was between 0.5 s and 1.5 s, depending on Wi-Fi network
strength. The video was clear at a range of 30 meters in 1080p, but compression was seen beyond 50 meters. Table V.
Represents the quality metrics used for remote operation and streaming quality.
Figure VII. V380 Pro Application Interface
Metric
Value / Observation
Bluetooth Control Latency
< 100 ms
Video Streaming Latency
0.5–1.5 s
Video Quality
1080p HD (≤30 m)
Signal Strength (RSSI)
Bluetooth: -60 dBm; Wi-Fi: -50 dBm
Video Frame Rate
~25 fps
Command Packet Loss Rate
<1%
H. Mustafa, S. Ur Rehman, M. Ahsan Shaikh
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 164-176
https://doi.org/10.36561/ING.30.11
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
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User Experience Rating
4.5 / 5
Control Operational Range
~15 m
Video Streaming Range
30–50 m
Table V. Remote Operation and Streaming Quality Metrics.
5.4 Battery Charging and Discharge Behavior. - The battery performance was monitored during the continuous
outdoor operation. The solar power received from the PV panel can provide a maximum current of 7 A under direct
sunshine, as depicted in Fig. VIII. During the active locomotion and video streaming, the battery voltage decreased
from 12.6 V, which is the fully charged voltage, to 11.9 V within 30 min. After the solar charging, the voltage returned
to 12.6 V, and the SOC returned to approximately 90% within the 50th minute, which indicates the positive energy
balance during the mission operation.
The SOC level was also estimated for the battery, based on voltage levels (11.9V-12.6V), and this is only an indicative
trend, since there is a level of uncertainty when voltage is used to calculate SOC levels, especially when dynamic load
is involved and internal resistance is a factor. Thus, the evaluation is based on the actual harvested and consumed
energy (Wh) rather than SOC levels.
This evaluation, based on actual harvested and consumed energy, confirms that the robot is able to sustain its operation
on flat, gravel, and dirt terrains without any additional power supply, under the given environmental conditions.
Figure VIII. Battery Performance Over Time
6. Conclusion and Future Directions. - In this paper, the authors have presented a solar-powered, terrain-adaptive
mobile robot that integrates energy harvesting, four-wheel drive, dual wireless communication, and passive thermal
management into a modular platform. The experimental results have shown the positive energy balance and reliable
operation on various types of terrain, indicating the potential of the robot to perform autonomous, off-grid monitoring.
There are several areas where the system can be improved. The future work may be focused on the improvement of
the energy efficiency of the system using adaptive solar tracking and energy recovery mechanisms, improvement in
the adaptability of the robot using advanced suspension mechanisms, and improvement in the communication
capabilities using mesh networking techniques. The integration of machine learning techniques to enable the robot to
navigate autonomously through the terrain and optimize the energy efficiency, as well as the reduction in the cost of
the system using efficient components, will enable the system to be scalable to perform the task in large numbers.
H. Mustafa, S. Ur Rehman, M. Ahsan Shaikh
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 164-176
https://doi.org/10.36561/ING.30.11
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
175
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H. Mustafa, S. Ur Rehman, M. Ahsan Shaikh
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 164-176
https://doi.org/10.36561/ING.30.11
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
176
Author contribution:
1. Conception and design of the study
2. Data acquisition
3. Data analysis
4. Discussion of the results
5. Writing of the manuscript
6. Approval of the last version of the manuscript
HM has contributed to: 1, 2, 3, 4, 5 and 6.
SUR has contributed to: 1, 2, 3, 4, 5 and 6.
MAS has contributed to: 1, 2, 3, 4, 5 and 6.
Acceptance Note: This article was approved by the journal editors Dr. Rafael Sotelo and Mag. Ing. Fernando A.
Hernández Gobertti.
