Arduino Self-Balancing Robot: DIY Build with MPU6050

The complementary filter is a computationally efficient sensor fusion technique that combines accelerometer and gyroscope data to estimate roll and pitch angles by exploiting their complementary strengths: accelerometers provide stable long-term estimates when the system is at rest (measuring gravity), while gyroscopes offer accurate short-term angular rate measurements without drift; the filter merges these estimates using a weighted combination where the weighting factor alpha (typically 0.02-0.05) determines how much trust is placed in each sensor, with lower values favoring gyroscope estimates and higher values favoring accelerometer estimates.

Sensor fusion combines accelerometer and gyroscope data to improve attitude estimation accuracy, as accelerometers drift during dynamic motion while gyroscopes suffer from long-term drift; this complementary approach addresses the limitations of using either sensor alone for reliable orientation tracking in applications like UAVs.

A complementary filter combines gyroscope and accelerometer data to improve angle measurement accuracy. The process involves taking raw gyroscope values, multiplying by time between readings to get degrees traversed, then adding this to the final angle calculation. For the accelerometer portion, trigonometry calculates tilt angles based on acceleration measurements. The final angle combines mostly gyroscope data (for short-term responsiveness) with a small portion of accelerometer data (for long-term stability), creating sensor fusion.

Sensor fusion combines data from multiple sensors (gyroscope and accelerometer) to achieve more accurate and reliable measurements than either sensor could provide alone; the complementary filter achieves this by taking a weighted average of the gyroscope's smooth but drifting angle measurements and the accelerometer's noisy but stable angle measurements, with an optimal gain value around 0.91-0.96 that balances noise reduction and drift minimization for stable drone flight.

This segment demonstrates how to combine multiple sensors to overcome individual limitations through sensor fusion. The complementary filter algorithm recursively combines gyroscope and accelerometer data: it calculates angle changes from gyroscope angular velocity, adds to previous estimates, then combines with accelerometer data using weighted averaging (90% gyroscope, 10% accelerometer). This approach leverages the gyroscope's short-term accuracy while using the accelerometer to correct long-term drift. The filter acts as a low-pass filter, allowing stable low-frequency signals while attenuating high-frequency noise. The result is a filtered angle that closely matches the encoder's direct measurement while being applicable to applications where encoders cannot be used, such as drones. This technique is fundamental in robotics, navigation, and autonomous systems.

This tutorial demonstrates how to add wireless Bluetooth connectivity to Arduino projects using HC-05 or HC-06 modules, covering hardware wiring with voltage level conversion (using a 2.2kΩ and 5.7kΩ resistor divider for 3.3V to 5V conversion), software setup using the SoftwareSerial library to create a serial communication channel on pins 8 and 9, and establishing connections through a mobile phone Bluetooth terminal app for bidirectional data transmission between the Arduino and mobile device.

Implement HC05 Bluetooth module as relay between HC12 and devices. Connect HC05 to FTDI, enter AT command mode by holding button during USB connection. Set HC05 baud rate to 38400 (default), then change to 57600 using AT+UART=57600,0,0. Connect HC05 RX to HC12 TX and vice versa. Power both modules via ST-Link. On computer, select Bluetooth COM port at 57600 baud in Mission Planner. For mobile access, pair HC05 via Bluetooth settings (default password '1234'), install Mission Planner on phone, select paired module, set baud rate to 57600, and connect. Execute arm/disarm commands from mobile device for complete wireless control capability.

The HC-05 Bluetooth module enables wireless communication between microcontrollers and smartphones, operating at 5V with 30mA current consumption and 9600 baud rate using UART protocol; it connects via four wires (5V to VIN, GND to GND, TX to GPIO 5 as RX, RX to GPIO 4 as TX) and can be controlled through Arduino IDE using SoftwareSerial library to transmit and receive data, enabling applications like remote LED control systems.

The HC-05 Bluetooth module enables wireless serial communication between Arduino boards and external devices such as smartphones or laptops by transmitting data through serial communication at a default baud rate of 38,400; when connecting to devices with different voltage levels (like 5V Arduino to 3.3V Bluetooth module), a voltage divider is required on the TX line to prevent damage, and the communication protocol involves sending ASCII characters (e.g., '0' for off, '1' for on) which are read by the Arduino and used to control connected components like LEDs.

The best Bluetooth module depends on your specific project requirements rather than a universal 'best' choice; HC-05 offers maximum flexibility as a master/slave module with customizable settings, HC-06 is simpler and cheaper for basic phone-controlled applications, HM-10 provides low-power Bluetooth 4.0 but may be complex for beginners, RN-42 is industrial-grade but expensive and overkill for simple projects, while ESP32 integrates Wi-Fi and Bluetooth in a single microcontroller at lower cost than separate modules.

An optic encoder converts angular motion into digital pulses by using an infrared LED and photo transistor to detect holes and fields on a rotating disc, enabling precise control of motor rotation angle, speed, and position through step counting; the system requires proper resistor values to ensure voltage levels meet microcontroller logic thresholds and the optic switch must operate at frequencies compatible with motor speed.

This comprehensive project demonstrates precise DC motor control using optical encoders for exact positioning. The system uses a H-bridge circuit with relays for bidirectional motor control, dual optical encoders (A and B) for position and direction detection, and limit switches for safety boundaries. Single sensors cannot determine rotation direction; two sensors with a specially designed disk (larger slot spacing) enable direction detection by comparing which sensor triggers first. The ESP32 microcontroller manages all connections with internal pull-up resistors. The program uses step-based counting (44 steps = 11 revolutions) instead of time-based control for reliable positioning regardless of load variations. This architecture enables precise control for applications like stage lighting systems where accurate positioning is critical.

A closed-loop stepper motor can be created by attaching an optical rotary encoder to a standard stepper motor's shaft, enabling position feedback for precise control; this mechanical modification involves extending the motor shaft, designing a custom mounting plate with CNC machining, and preparing mounting holes and threads to secure both the motor and encoder together.

Encoders are electromechanical devices measuring angular position through rotating discs and sensors. Single-phase encoders provide basic rotation detection but cannot determine direction. Optical encoders use infrared LEDs and receivers separated by slotted discs, producing square wave outputs where high states indicate light passing through slots and low states indicate blocking. This fundamental principle enables pulse counting for RPM calculation and position tracking.

DC gear motors can achieve positional accuracy similar to stepper or servo motors by adding an encoder. An encoder provides feedback about the motor's actual position, enabling closed-loop control that ensures precise positioning. This transforms a simple DC motor into a synthetic device capable of accurate location control, expanding the capabilities beyond basic speed control to include precise positioning applications.

This video demonstrates how dynamic stability in bipedal robots can be achieved through multi-axis control systems that integrate inertial measurement units (IMUs) with feedback-driven actuators. The presenter builds upon previous work by adding front-to-back stability using a dedicated actuator controlled by an IMU, addressing issues of rocking and oscillation caused by loose actuators. Key innovations include differential gain control between hip and ankle joints to prevent tipping, leg bending mechanisms that absorb impact loads, and hard end stops on rubber feet to maintain consistent ground contact. The robot demonstrates the ability to stabilize itself dynamically in motion by measuring its orientation in space and coordinating all eight motors to dampen unwanted movement, showing that effective bipedal stability requires simultaneous control across multiple axes with carefully tuned feedback loops.

Ballbots are robots that balance and move on a single ball, achieving dynamic movement through inherent instability; they maintain balance by continuously measuring their pitch angle and counteracting toppling via motor adjustments at 160 times per second, while three wheels on the ball enable omnidirectional movement and rotation.

Reaction wheels spin a large mass around a stationary center point to create reaction forces that push the robot in the opposite direction. By measuring the robot's angle and dynamically controlling the reaction wheel speed, a device can actively balance itself. This principle is used in balancing robots and can be applied to tripod robots to allow them to balance on two legs while taking steps with the third leg.

This video demonstrates how a bipedal humanoid robot achieves dynamic stability through the integration of multiple RC gyroscopes (Hobby King HK 401) wired in series with servo motors, where gyroscopes on the thighs, knees, and ankles continuously monitor angular motion and compensate for disturbances by adjusting servo positions to maintain balance during movement.

Ball-balancing robots require dynamic stability to remain upright, which means they must continuously adjust their motor speeds and positions approximately 100 times per second. This rapid adjustment is necessary because any imbalance causes the robot to tip over. The system reads data from an inertial measurement unit (IMU) containing both a gyroscope and accelerometer, then uses a controller to make real-time adjustments to all six axes of motion simultaneously.