Designing a Low-Cost 6-DOF Robotic Arm for Robotics and Control Education

Modern robotics education faces a persistent challenge: the concepts are increasingly sophisticated, but the hardware required to teach them remains inaccessible to many institutions. Industrial-grade educational manipulators can cost thousands of dollars per unit, placing them beyond the reach of many universities and researchers, particularly in developing regions. This project set out to explore a different approach: designing a modular, open-source, six-degree-of-freedom robotic arm that could serve as a practical platform for teaching robotics, control systems, and mechatronics.

Motivation

The goal was not simply to build a robotic arm. The objective was to create a platform capable of exposing the complete robotics stack, from mechanical design and actuator selection to kinematics, dynamics, control theory, and embedded systems integration.
To achieve this, the system needed to satisfy several key requirements:

• 1. Six degrees of freedom to emulate industrial manipulators.

• 2. Open access to low-level control and sensor data.

• 3. Compatibility with MATLAB, ROS2, and other research tools.

• 4. Modular hardware architecture for future upgrades.

• 5. Affordable construction using 3D-printed components and off-the-shelf electronics.
  

The resulting platform was designed as both a teaching tool and an experimental research platform.

Mechanical Design

The robotic arm consists of six revolute joints arranged in a serial manipulator configuration. Most structural components were designed for additive manufacturing, enabling rapid iteration and low fabrication costs.
A significant engineering challenge was balancing weight, strength, and actuator capability. Since every joint supports the mass of downstream links, torque requirements increase dramatically toward the base of the arm. To address this, multiple motor types were evaluated using a weighted decision matrix considering factors such as cost, position control capability, smoothness of motion, and suitability for advanced control methods.
Brushed DC motors equipped with quadrature encoders were ultimately selected. While servos and stepper motors offered certain advantages, DC motors provided the greatest flexibility for implementing a wide range of control strategies while maintaining affordability.

Kinematic Modelling

One of the primary educational objectives of the project was exposing students to robotic kinematics.
Rather than relying exclusively on Denavit-Hartenberg parameters, the arm's forward kinematics were derived using successive homogeneous transformation matrices. This approach provided a more intuitive representation of coordinate frame relationships while remaining mathematically rigorous.
The kinematic model allows computation of the end-effector pose directly from joint positions, forming the foundation for:

• Forward kinematics

• Inverse kinematics

• Workspace analysis

• Trajectory generation

• Motion planning
  

By developing these models from first principles, the project demonstrates how geometric transformations translate directly into robot motion.

Dynamic Modelling Using Euler-Lagrange Formulation

To support advanced control methodologies, a full dynamic model of the manipulator was developed using the Euler-Lagrange energy-based approach.
The process involved deriving the system's kinetic and potential energy expressions and then applying the Euler-Lagrange equations to obtain the manipulator dynamics. This resulted in a model capable of representing:

• Inertial effects

• Coriolis and centrifugal forces

• Gravitational loading

• Joint torque requirements
  

Developing the dynamic model was particularly valuable because it establishes the theoretical foundation for model-based control techniques such as computed torque control and gravity compensation.

Experimental Motor Characterisation

A practical challenge emerged during development: the selected motors lacked sufficient manufacturer data for accurate control system design.
To overcome this limitation, a series of system identification experiments were performed. Step-response tests were conducted on each motor, allowing first-order transfer functions to be estimated directly from experimental data.
From these experiments, key parameters such as gain, time constant, torque constant, and back-EMF constant were determined.
This process transformed inexpensive commodity motors into mathematically characterised actuators suitable for simulation and controller development.

Electronics and Control Architecture

The control architecture was designed around an ESP32 microcontroller supported by a distributed sensing and actuation network.
The system incorporates:

• Quadrature encoders for position feedback

• INA219 current sensors for motor monitoring

• Motor drivers for each joint

• Limit switches for safety and homing

• PWM expansion through a PCA9685 controller

Current sensing was included intentionally to support future implementation of torque-based and model-based control algorithms.
A key design philosophy throughout the project was transparency. Rather than abstracting away hardware details, the platform exposes raw sensor data and control interfaces, allowing students and researchers to interact directly with the underlying system.

Simulation and Validation

Before fabrication, the complete robotic arm was analysed in simulation.
Finite Element Analysis (FEA) was used to evaluate structural stresses and identify potential failure points under static loading conditions. Kinematic simulations were used to verify workspace reachability, while dynamic simulations provided insight into joint torque demands and controller performance.
Several control approaches were investigated, including:

• PID Control

• Internal Model Control (IMC)

• Gravity Compensation

• Computed Torque Control

These simulations established a framework for comparing classical and model-based control strategies on the same physical platform.

Key Engineering Lessons

One of the most important lessons from this project was that robotics is fundamentally a systems engineering problem. Mechanical design decisions influence actuator requirements. Actuator characteristics affect controller design. Control requirements dictate sensing and computational needs.
Building a complete robotic system required integrating concepts from mechanics, electronics, embedded systems, mathematics, control theory, and software engineering into a single functioning platform.
The result is not simply a robotic arm, but a learning and research platform that makes these interconnected engineering disciplines visible and accessible.

Looking Ahead

Future development will focus on expanding the platform's capabilities through stereo vision, ROS2 integration, onboard computation using a Raspberry Pi, custom PCB development, and interchangeable end effectors.
The long-term vision is to create an affordable, open, and extensible robotics platform capable of supporting everything from introductory robotics courses to advanced research in manipulation, computer vision, and intelligent control systems.

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