An Arduino-Based Robot for Wall Painting Tasks

An Arduino-Based Robot for Wall Painting Tasks

Vijaya Kumar S, Sharath Kumar K, Chirag M, Chandan Gowda VM, Kiran T

Mechanical Department, M.S. Ramaiah University Of Applied Sciences, Bengaluru, India

DOI: https://doi.org/10.51244/IJRSI.2025.120700164

Received: 18 June 2025; Accepted: 23 June 2025; Published: 14 August 2025

ABSTRACT

This project presents a robot designed to automate painting tasks in construction and maintenance industries. The robot utilizes BLDC fans for wall-climbing suction, enabling movement across vertical surfaces without scaffolding or ladders. Built with a lightweight 3D Printed Plastic chassis, the system integrates Arduino UNO microcontroller, IR sensors for obstacle detection, and Bluetooth connectivity for mobile app control. The paint delivery system features a spray mechanism with external tank for uniform coverage. This solution addresses safety hazards, labour costs, and time inefficiencies associated with traditional painting methods. The robot demonstrates practical robotics applications, offering enhanced safety, reduced operational costs, and improved efficiency in wall painting operations.

Keywords—wall painting, robot, wall climbing, paint robot

Aim – To develop Robot capable of climbing and painting walls.

PROBLEM STATEMENT

Need of the Project

The construction industry faces critical challenges in wall painting operations, particularly in high-rise buildings where traditional methods pose significant safety risks and operational inefficiencies. Manual painting requires high risk, multiple workers, and prolonged project timelines, resulting in high labor costs and increased accident potential. Current methods are inadequate for large-scale applications, in construction schedules and compromising worker safety. The growing emphasis on automation and workplace safety standards demands innovative solutions. An autonomous wall painting system is essential to eliminate human exposure to high heights, reduce labor dependency, accelerate project completion, and ensure consistent paint quality while meeting modern construction industry requirements for efficiency and safety.

Motivation

Wall painting in tall buildings is dangerous and takes too much time. Workers need scaffolding and ladders, which can cause accidents. It also costs a lot of money and requires many people to complete the job.

We wanted to create a robot that can paint walls safely without putting workers at risk. The robot can climb walls by itself and paint them automatically. This saves time, reduces costs, and keeps workers safe from falling.

The construction industry needs better tools to make work easier and safer. Our robot can help solve the problem of painting tall buildings while making the job faster and more efficient. This project shows how simple technology can help solve everyday problems in construction work.

Applications

Some of the applications where the Autonomous Wall painting robot can be used are Skyscrapers, Hospitals, Schools, Warehouses, Factories, Hotels, Apartments, Offices, Stadiums, Bridges

INTRODUCTION

Traditional wall painting in large buildings is labor-intensive, time-consuming, and hazardous, requiring scaffolding, ladders, and extensive manpower. This creates significant costs and safety risks. Our Autonomous Wall Painting Robot addresses these challenges by automating the painting process with minimal human intervention.

The system features a lightweight 3D Printed Plastic chassis that uses powerful BLDC fans to generate suction for climbing and moving across vertical surfaces. An Arduino UNO microcontroller manages operations, while IR sensors provide obstacle detection. Users control the robot via a Bluetooth-enabled mobile application that monitors battery status, connectivity, and paint spraying functions.

The integrated paint system includes a spray mechanism and external tank for even distribution. This innovation enhances safety, reduces costs, and significantly improves efficiency compared to traditional methods. Our robot demonstrates practical robotics applications in construction, showcasing how interdisciplinary engineering can modernize everyday industrial tasks while addressing real-world challenges.

In addition, components like the spray nozzle, suction system, and IR sensors are mounted for quick replacement, enabling adaptability for different wall textures and architectural layouts. The microcontroller is programmed with optimized motion algorithms to ensure uniform paint coverage, contributing to a professional finish. The Bluetooth control system includes a user-friendly interface that allows operators to switch between manual and autonomous modes seamlessly. This flexibility ensures control in dynamic environments while maintaining autonomy in repetitive tasks. Furthermore, the compact size of the robot makes it suitable for indoor and outdoor painting jobs, even in narrow or elevated space.

PRINCIPLES AND ASSUMPTIONS

Principles

• Simple Autonomous Functionality – The robot should perform basic autonomous wall movement and painting.
• Safe Wall Climbing Mechanism – BLDC fan-based suction should be safe for residential walls (like plaster or painted drywall).
• Lightweight and Portable Design – The robot must be compact, lightweight, and easy to carry and set up in small home or office settings.
• Affordable Materials – Use low-cost materials like acrylic or lightweight Plastic for the chassis to maintain affordability without compromising structural integrity.
• Moderate Paint Efficiency – Ensure coverage with minimal control—Uniform or fancy pattern optimization isn’t necessary at this scale.

Assumptions

• Robot is assumed to work on limited workspace of area and project is shown on flat vertical surface (e.g. Wood, hard board, flat boards).
• Walls are assumed to be smooth and flat, with no heavy texture (like brick or stone) to allow suction to hold properly.
• Operation is assumed to be indoors or in protected areas—no exposure to rain, dust, or wind.
• Access to a 1 Power connection (from laptop/power bank) for Arduino, sensors and N20 motors,1 Cell of 9v for pump and one 48v Battery for BLDC motor is available for operation.
• Robot is assumed to work in short sessions (up to 1hour with* full battery).
• Paint used will be standard water-based or acrylic paint suitable for spraying without clogging.

OBJECTIVES

• To analyse existing research and market trends in wall painting automation, identifying functional gaps and opportunities for innovation.
• To define precise technical and functional requirements, including parameters such as payload and suction pressure, and to select the most suitable components for the application.
• To create a conceptual diagram illustrating the operational workflow of the robot.
• To develop an optimized design that balances functionality, payload capacity, and structural durability for practical usability.
• To test the prototype under various conditions and validate its functionality and reliability through live demonstrations.

Literature Review – Summary

Wall-climbing robots have emerged as a significant advancement in painting automation, offering notable advantages over conventional robotic systems. Traditional methods, such as rail-based mechanisms and cable-suspended robots, often require extensive pre-installed infrastructure or anchoring systems, limiting their adaptability and increasing setup complexity. In contrast, climbing robots adhere directly to vertical surfaces using advanced adhesion mechanisms, providing greater operational flexibility and mobility.

Previous research by Selvamarilakshmi et al. (2015) and Zaid and Selvakumar (2016) has outlined the inherent limitations of these traditional approaches, particularly their dependency on external support structures and their restricted movement across variable surfaces. Similarly, Kolekar et al. (2015) explored Cartesian robots for wall painting, noting their effectiveness in workspace coverage but highlighting their inability to adapt to irregular or non-linear surfaces.

In contrast, our robot is designed to overcome these limitations by incorporating a ducted-fan-based suction system, enabling stable adhesion on smooth vertical surfaces without the need for external support. Unlike earlier models, our design emphasizes portability, affordability, and indoor usability with a lightweight 3D-printed chassis, which makes it practical for small-scale and medium-scale projects, especially in domestic or commercial settings.

Moreover, while most previous studies focus on the mechanical or adhesion aspects alone, our work integrates a full system—combining wall climbing, obstacle detection via IR sensors, mobile-app-based wireless control, and an automated paint spraying mechanism. This holistic integration improves functionality and usability in real-world scenarios.

Our approach also improves safety by reducing reliance on ladders and scaffolding and lowers operational costs through semi-autonomous operation. Though challenges such as adhesion reliability, power efficiency, and full autonomy remain, the presented prototype lays a strong foundation for scalable solutions and further enhancements using AI-based path planning and surface adaptability.

By focusing on practical deployment in typical indoor environments and incorporating interdisciplinary design principles, our work offers a more user-friendly and implementable solution compared to earlier studies, marking a step forward in making automated wall painting both accessible and effective.

Specifications

Table 1 outlines the comprehensive technical specifications of the Autonomous Wall Painting Robot, detailing its mechanical structure, electronic components, sensors, and functional parameters. Each element listed has been specifically selected and implemented to suit the operational requirements of this project, ensuring optimal performance across all automated painting tasks.

Component	Specifications	Function
BLDC Motor	• Voltage: 7.4-11.1V	Main propulsion / lift mechanism
	• Current: 10-12A
	• Speed: 980KV
	• Shaft:3.17mm
	• Weight: 50g
BLDC ESC	• Current: 30A continuous	Motor speed control
	• Voltage: 2-3SLiPo (7.4-11.1V)
	• BEC: 5V/2A
	• PWM frequency: 50Hz
Propeller	• Diameter: 10cm (4 inch)                         • Material: Plastic	Thrust generation
	• Shaft hole: 3.17mm
N20 Motors	• Voltage: 3-6V	Wheel drive motors
	• Speed: 100RPM                              • Torque: 0.8 kg·cm
	• Current: 150mA
	• Shaft: 3mm D-type
IR Sensor	• Voltage: 3.3-5V	Obstacle detection
	• Range: 2-30cm
	• Output: Digital/Analog
	• Current: 7mA
Servo Tester	• Voltage: 4.8-6V	Manual motor control
	• Output: PWM signal
	• Frequency: 50Hz
	• Channels:3
Microcontroller	• Voltage: 7-12V input	Main control unit
	• Logic: 5V
	• Digital I/O: 14 pins
	• Analog: 6 pins
	• Flash: 32KB
Bluetooth Module	• Voltage: 3.6-6V	Wireless communication
	• Range: 10m
	• Baud rate: 9600-115200                 • Protocol: SPP
Paint Pump	• Voltage: 12V DC	Paint delivery
	• Flow rate:1.2L/min
	• Pressure: 0.4MPa                            • Current: 1A
Spray Nozzle	• Material: Plastic	Paint spraying
	• Orifice: 1-2mm
	• Thread: Standard
	• Pattern: Adjustable

Table 1. Specifications of the Model

Concept and Model Development

Fig. 1. CAD Model

Fig. 2. CAD Model Assembly Draft

Table 2.  Bill of Materials

Block Diagram

Model Block Diagram

The above Fig. 3. Shows the system architecture with Arduino Uno as the main controller, Input sensors (IR sensor, Bluetooth module, manual switch), Output actuators (BLDC motor system, N20 wheel motors, paint pump, roller), Power control through relay modules.

Circuit Diagram

Model Circuit Diagram

The circuit diagram illustrates the complete electrical connections and component integration. It shows the Arduino UNO microcontroller as the central processing unit, connected to BLDC for wall adhesion. Bluetooth module for wireless communication, motor drivers for movement control, and the paint spraying system.

 

Arduino Pin	Component	Function
D0 (RX)	HC-05 TX	Bluetooth Receive
D1 (TX)	HC-05 RX	Bluetooth Transmit
D2	Relay 1	Paint Pump Control
D3	Relay 2	Spray System Control
D4	BLDC ESC	Motor Speed Control
D5	N20 FL	Front Left Motor
D6	N20 FR	Front Right Motor
D7	N20 RL	Rear Left Motor
D8	N20 RR	Rear Right Motor
D11	Buzzer	Audio Feedback
D12	Manual SW	Emergency Stop
A0	IR Sensor	Distance Measurement

Arduino Pin Connections

Table 2. Pin connections

The following commands are assigned for the Bluetooth terminal application, as illustrated in Fig. 5:

F – Move Forward

B – Move Backward

L – Turn Left

R – Turn Right

S – Stop Movement

Control Buttons

Test and Demonstarion

Developed Model

Results and Calculations

Table 3. Model Parameters

Wheel Circumference (Distance per Rotation)

The circumference of the wheel determines how far the robot moves in one full rotation of the wheel.

Circumference = π × Diameter

Circumference = 3.1416 × 42mm = 131.94 mm​ ≈ 13.1cm​

So, the robot moves 13.1 cm for each full wheel rotation.

Speed required to paint a wall of given length

Testing the wall length is 0.4 meters (40 cm), and the robot completed at 11.58 Seconds:

Speed = 400 / 11.58 = 0.03454 m/sec ≈ 3.454 cm/sec

Force Required to Climb Vertically

F = m × g

F = 0.5 × 9.81

F= 4.905 N

Total Torque Required

Total Torque = F × r

Total Torque = 4.905 × 0.021 = 0.103 N⋅m

Torque per motor = 0.103 ÷ 4 = 0.026 N⋅m

Since the robot moves at 0.03454 m/sec,

In 1 second, it paints a line that’s 0.03454 m long.

Area painted per second

Area/sec = Speed × Spray Width

             = 0.03454 m/sec × 0.1 m

             = 0.003454 m²/sec

Area per minute

Area/min = 0.003454 × 60

           = 0.20724 m²/min

With 1.2 L of paint per minute, this gives:

Paint coverage efficiency:

Test Parameter	Result
Total paintable area	~0.20724 m²/min flow
Battery runtime (Arduino & N20)	~1 hour (powered by external USB or power bank)
Pump battery (9V)	~30–40 minutes of continuous spraying
BLDC fan battery (48V)	~15 minutes suction operation before recharge
Suction test (vertical surface)	Holds firmly for >10 minutes on smooth wood/ply
Obstacle avoidance	IR sensor detects obstacles within 25 cm reliably
Paint quality	Even spray pattern with minimal overspray

       0.20724 m² / 1.2 L ≈ 0.1727 m²/L

Parameter	Value
Payload Mass – m	0.5 kg
Gravity – g	9.81 m/s²
Wheel Radius – r	21 mm = 0.021 m
Motors Used	4 × N20 motors (100 RPM)
Paint Pump Flow Rate	1.2 L/min
Spray Pattern Width (approx.)	10 cm (0.1 m)

Table 4. Experimental Results

Criteria	Manual Painting	Our robot
Labor Required	2–3 people	1 operator (semi-autonomous)
Safety Risk	High (working at height)	Low (no ladders/scaffolding needed)
Time Efficiency	~2–3 hours / 10 m²	~1.5 hours / 10 m²
Cost	Moderate (daily wages)	Low (one-time investment)
Coverage Quality	Variable	Uniform spray
Wall Access	Limited to reachable areas	Smooth vertical indoor walls

Table 5. Manual Painting vs Robot Comparison

FUTURE SUGGESTION

To enhance the capabilities of the Autonomous Wall Painting Robot and address its current limitations, several key improvements should be considered:

Power and Adhesion Systems: Develop hybrid power solutions that combine high-capacity batteries for extended operation. Integrate backup adhesion mechanisms, such as magnetic systems for metal surfaces or gecko-inspired adhesion, to improve reliability during power fluctuations.

Surface Adaptability: Design adjustable suction chambers and flexible chassis components to handle textured walls, corners, and irregular surfaces. Develop weather proof housing for outdoor applications with enhanced resistance to environmental conditions.

Advanced Paint Systems: Incorporate multi-colour paint reservoirs with automated mixing capabilities, pressure sensors for consistent spray patterns, and paint thickness monitoring for quality control.

Scalability: Design modular systems that allow multiple robots to work collaboratively on large-scale projects, with centralized coordination and synchronized operations to enhance productivity and coverage efficiency.

CONCLUSION

The project titled “An Arduino-based Robot for Wall Painting Tasks” was undertaken with the primary objective of designing a self-operating robot capable of climbing walls and performing painting tasks. Through this endeavour, a functional prototype was developed that successfully achieved autonomous wall climbing and efficient paint application. The completion of this project marks a significant step toward introducing automation into traditionally labour-intensive and hazardous tasks such as wall painting.

The robot is built around a lightweight and durable chassis made of 3D-printed plastic material, ensuring it can support various essential components without compromising mobility. The climbing mechanism, which uses Brushless DC fans, enables the robot to adhere to vertical surfaces by creating consistent suction force. This was one of the core technical challenges of the project and was successfully addressed through rigorous testing and optimization of airflow and fan configuration.

Mobility on the wall is achieved with movement in all directions – forward, backward, left, and right, enabling the robot to cover a defined work surface area. The painting system consists of a mounted spray nozzle. The robot’s electronics, controlled by the Arduino UNO microcontroller, enable wireless communication with a mobile app, giving users real-time control over movement. Additional features such as IR obstacle detection and automated control logic further enhance the robot’s ability to perform tasks reliably and safely.

The mobile application features a simple, user-friendly interface that allows even non-technical users to operate the robot with ease. The integration of hardware and software components was accomplished through progressive development and iterations made throughout the project.

The robot’s performance demonstrated advantages in terms of time savings, safety, and consistency compared to traditional manual painting methods. By reducing the need for scaffolding or ladders, the risk associated with working at heights is significantly minimized. While the current prototype is primarily suitable for smooth indoor walls and small-scale projects, it lays the foundation for more advanced iterations that could handle varied textures, outdoor conditions, and autonomous navigation across complex wall geometries.

However, the project also revealed certain limitations. The reliance on a stable power supply for the suction mechanism means that even small fluctuations in power can cause the robot to lose adhesion and fall. Additionally, the limited battery runtime restricts its ability to operate over large areas or for extended periods. The system is also currently dependent on user input for navigation, lacking full autonomous path planning. These limitations point toward opportunities for future research and enhancement, such as integrating computer vision for wall mapping, improving suction efficiency, or exploring alternative adhesion methods like magnetic systems or gecko-inspired technologies.

ACKNOWLEDGMENT

The members of this project would like to extend our heartfelt gratitude to all those who have been instrumental in the successful completion of this work. Their unwavering support and guidance have been invaluable throughout this journey.

First and foremost, we would like to express our profound appreciation to our academic supervisor, Asst. Prof. S. Vijaya Kumar. Your mentorship, dedication, and wealth of knowledge have been a constant source of inspiration. Your insightful feedback and encouragement have truly shaped the outcome of this project.

We would also like to extend our sincere thanks to the Head of the Department, Dr. Dayananda B. S., for fostering an environment conducive to research and academic excellence. Your leadership and commitment to the department’s growth have had a significant impact on our academic development.

Our heartfelt gratitude goes to the Dean of the Faculty of Engineering and Technology, Dr. Sarat Kumar Maharana, and the Department of Mechanical Engineering for their consistent support and for providing the resources and encouragement necessary to carry out this project successfully.

Lastly, we would like to express our sincere appreciation to all the faculty members, parents, and friends for their moral support and continuous encouragement during the course of this project.

In conclusion, this acknowledgment is a small token of our appreciation for everyone who contributed to the successful completion of our work. Your belief in our abilities and your support have been truly instrumental, and we are genuinely thankful for your contributions.

REFERENCES

1. Kumar, V., Michael, N. and Kumar, M. (2012) Opportunities and Challenges with Autonomous Micro Aerial Vehicles. The International Journal of Robotics Research, 31(11), pp.1279–1291.
2. Siciliano, B. and Khatib, O. (2016) Springer Handbook of Robotics. 2nd edn. Cham: Springer.
3. Hamner, B., Koterba, S., Plagemann, C., Kenney, J. and Konolige, K. (2010) Robot Localization and Navigation with a Stereo Camera System. In: ICRA 2010. Anchorage, AK: IEEE, pp. 5791–5797.
4. Dutta, S. and Mandal, A. (2016) Wall Climbing Robot for Surveillance and Inspection. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 4(7), pp.151–154.
5. Hwang, S. and Yuta, S. (2008) Development of a Wall Painting Robot with Wall Surface Detection. Journal of Robotics and Mechatronics, 20(1), pp.109–116.
6. Pathak, N., Sharma, R. and Rathi, A. (2020) Design and Fabrication of Wall Painting Robot. International Journal of Engineering Research & Technology (IJERT), 9(5), pp.131–134.
7. Ghosh, S. and Chakrabarti, S. (2019) Design of a Low-Cost Autonomous Painting Robot Using IoT and Image Processing. In: 2019 International Conference on Advances in Computing, Communication and Control (ICAC3). Mumbai: IEEE, pp.1–5.
8. Bogue, R. (2018) Robots for Painting and Coating: A Review of Technologies and Applications. Industrial Robot: The International Journal of Robotics Research and Application, 45(6), pp.673–678.
9. Hu, X., Gao, S., Wang, J. and Jiang, G. (2016) Development of a Climbing Robot with Negative Pressure Adhesion for Steel Surface Inspection. International Journal of Advanced Robotic Systems, 13(5), pp.1–12.
10. Hossain, M.A. and Mohammed, I. (2019) Autonomous Navigation Robot: Implementation of Wall Following Behavior Using Ultrasonic Sensor. Journal of Engineering Research and Reports, 5(2), pp.1–8.
11. Park, J., Oh, S. and Ahn, H. (2013) Development of an Automated Painting Robot System for Interior Wall Finishing. Journal of Mechanical Science and Technology, 27(10), pp.2995–3002.
12. Gharehbaghi, A. and M. McManus, K. (2017) Automation in Construction: Current Developments and Future Outlook. Procedia Engineering, 180, pp.1452–1459.
13. Sorour, M., Abdellatif, M., Ramadan, A. and Abo-Ismail, A. (2011) ‘Development of Roller-Based Interior Wall Painting Robot’, Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 1–5.
14. Madhira, K., Mehta, S., Bollineni, R. and Kavathia, D. (2017) ‘AGWallP — Automatic Guided Wall Painting System’, International Journal of Engineering Research & Technology (IJERT), 6(11), pp. 1–4.
15. Nayana, C.G., Nandini, M., Mamatha, D. and Nandini, S. (2019) ‘Development of Autonomous Wall Painting Robot using Raspberry Pi and Image Processing’, International Journal of Engineering Research & Technology (IJERT), 8(11), pp. 1–5.
16. Mukundan, V. and Sirajudeen, M. (2017) ‘Automatic Sensor Based Wall Painting Robot’, International Journal of Engineering Research and Applications, 7(1), pp. 49–56.
17. Dipta, D.D., Kundu, D., Rahman, A., Rahman, M. and Sazzad, S. (2024) ‘A Remote Control Painting System for Exterior Walls of High-Rise Buildings through Robotic System’, arXiv preprint arXiv:2409.05153.
18. Chen, G., Al-Haddad, T., Dellaert, F. and Hutchinson, S. (2024) ‘Architectural-Scale Artistic Brush Painting with a Hybrid Cable Robot’, arXiv preprint arXiv:2403.12214.
19. Schaldenbrand, P., McCann, J. and Oh, J. (2022) ‘FRIDA: A Collaborative Robot Painter with a Differentiable, Real2Sim2Real Planning Environment’, arXiv preprint arXiv:2210.00664.
20. Lindemeier, T., Pirk, S., Tautzenberger, M. and Deussen, O. (2013) ‘e-David: An Autonomous Painting Robot’, Leonardo, 46(1), pp. 5–12.
21. Moura, L. and Pereira, H.G. (2004) ‘Man and Robots: Symbiotic Art’, Institut d’Art Contemporain, Villeurbanne
22. Al Mawali, A.A.A. and Hussain, S.M. (2023) ‘Automated Wall Painting Robot for Mixing Colors Based on Mobile Application’, International Journal of Engineering and Manufacturing (IJEM), 13(1), pp. 33–44.
23. Ganga Devi, P., Muthu Meenakshi, M., Vasuki, B. and Thangam, E. (2021) ‘Design and Implementation of Autonomous Paint Spraying Machine Using PLC’, International Journal of Scientific Research in Science and Technology (IJSRST), 9(1), pp. 694–698.
24. Krishna, B.S., Chandrashekar, M., Sandeep Kumar, B.V. and Vinay Kumar, P. (2019) ‘Fabrication of Automatic Wall Painting Machine’, International Journal of Engineering Research & Technology (IJERT), 7(7), pp. 1–5.