System for gesture-controlled automatic stabilization and navigation of a quadcopter using Arduino and computer vision

Abstract

System for gesture-controlled automatic stabilization and navigation of a quadcopter using Arduino and computer vision, consisting of: A gesture recognition unit includes a camera configured to capture real-time video of a user's hand. a processing unit operatively connected to the camera, the processing unit configured to execute image processing and gesture classification algorithms to recognize predefined hand gestures and generate corresponding flight control commands. a wireless communication interface configured to transmit the flight control commands from the processing unit to the quadcopter. a quadcopter that includes: a frame structure with four engines and propellers arranged in a cross configuration; a microcontroller-based flight controller mounted on the frame; a sensor module that uses a gyroscope and an accelerometer configured to detect pitch, roll, and yaw; and stabilization control logic executed by the flight controller, the logic configured to process sensor data in real time and dynamically adjust motor speeds to maintain the orientation of the quadcopter; wherein the flight controller is further configured to receive the flight control commands from the gesture recognition unit and map the commands to motor control signals for directional flight, The system enables the quadcopter to navigate and stabilize in real time based on contactless hand gestures, without the need for a conventional remote control or wearable device.

Description

[0001] The present invention relates to unmanned aerial vehicles (UAVs) and human-machine interaction systems. More particularly, it relates to a gesture-controlled, self-stabilizing quadcopter that combines computer-aided gesture recognition, wireless real-time communication, and low-cost, microcontroller-based flight stabilization.

[0002] Unmanned aerial vehicles (UAVs), commonly known as drones or quadcopters, have gained significant traction in surveillance, environmental monitoring, logistics, and education due to their maneuverability, affordability, and ease of use. Conventional quadcopters are typically controlled via physical remote control transmitters or mobile apps, requiring user training, visual coordination, and constant manual input. Such systems limit intuitive control, particularly for non-technical users or those with limited physical access to standard control devices. While advances in human-machine interaction have introduced voice-, motion-, and gesture-based control systems for various platforms, most UAVs still lack robust and reliable gesture-controlled interfaces.Existing gesture-based control methods often rely on external sensors, wearable devices, or dedicated motion controllers that require bound hardware or complicated calibration, limiting portability and usability. At the same time, simple quadcopter models, especially those powered by microcontrollers such as Arduino, struggle to achieve autonomous flight stability. Without additional sensor integration or complex control systems, these drones are highly susceptible to drift and instability during hovering or directional movement, making them unsuitable for gesture-based or autonomous applications without constant manual correction.Although individual technologies such as computer vision, microcontroller-based quadcopters, and hand gesture recognition exist independently, there is currently no cost-effective, compact solution that effectively combines computer vision-based gesture recognition with onboard stabilization algorithms for real-time UAV control. Accordingly, there is a need for a gesture-controlled quadcopter system that provides real-time stabilization, eliminates dependence on physical controllers or wearables, and utilizes affordable and accessible components. The present invention addresses this need by introducing a modular, image-based quadcopter control system equipped with auto-stabilization capabilities and based on open-source hardware and software platforms.This enables more intuitive and accessible aerial control, with potential applications in surveillance, inspection, education and recreation.

[0003] To solve this problem, the present invention provides a system for gesture-controlled auto-stabilization and navigation of a quadcopter using Arduino and computer vision.

[0004] The system enables gesture-based control of a quadcopter and includes real-time auto-stabilization using Arduino-based hardware in conjunction with computer vision algorithms.

[0005] The system enables intuitive and contact-free navigation through the recognition of predefined hand gestures using an image processing model integrated into a camera module or an external processing unit.

[0006] The system ensures stable flight by using on-board sensors, including gyroscopes and accelerometers, to detect changes in orientation and compensate for external disturbances.

[0007] The system offers an affordable, modular, and open-source solution that eliminates the need for traditional remote controls or portable input devices.

[0008] The system is designed to be scalable and adaptable for use in education, monitoring, inspection and research, with the potential for future extensions such as obstacle avoidance, voice control or autonomous path planning.

[0009] The system implements an image processing-based control interface that improves accessibility and enables even non-technical users to operate UAVs using natural hand gestures.

[0010] In one embodiment, the present invention provides a system for gesture-controlled flight and automatic stabilization of a quadcopter using low-cost Arduino microcontrollers and computer vision-based hand gesture recognition. The system eliminates the need for traditional remote controls or physical contact interfaces by allowing users to control the drone using predefined hand gestures captured via a real-time video feed. The system consists of a quadcopter frame equipped with an Arduino-based flight controller, a gyroscope, and accelerometer modules for real-time orientation acquisition and stabilization. A ground-based computer or embedded vision module processes video input from a camera, applies image processing techniques, and executes a trained gesture recognition model to recognize user gestures.The detected gestures are converted into directional flight commands (e.g., up, down, forward, backward, left, and right) and transmitted wirelessly to the quadcopter via a serial or radio frequency (RF) communication interface. Flight stability is ensured by a closed-loop control system using IMU (Inertial Measurement Unit) data, which dynamically adjusts motor speeds to counteract external disturbances. The modular software architecture enables customizable gesture sets, dynamic thresholds, and user-specific calibration. The system is designed to be energy-efficient, portable, and reproducible using open-source hardware and software platforms.

[0011] The invention is explained again below.

[0012] The present invention relates to a system for gesture-controlled flight and automatic stabilization of a quadcopter that integrates an Arduino-based embedded controller with computer vision-based gesture recognition. The system enables contactless and intuitive control of the drone by interpreting human hand gestures in real time and translating them into navigation commands for the quadcopter. The invention comprises two main subsystems: a ground-based gesture recognition unit and a flight control module mounted on the quadcopter. The gesture recognition unit uses a standard camera module, e.g., a webcam, connected to a computer or microcontroller platform capable of executing image processing and machine learning algorithms. The video footage captures the hand movements within a specific field of view, and OpenCV-based techniques are used to process the images.The user's hand is separated from the background using methods such as background subtraction and color thresholding in HSV (hue, saturation, value) space. The processed images are passed to a trained machine learning model or rule-based logic to classify hand gestures such as palm up, palm down, swipe left, swipe right, or stop signal. These gestures are mapped to the corresponding directional commands—e.g., takeoff, landing, ascend, descend, turn, or hover—and transmitted wirelessly to the onboard flight controller via serial, Bluetooth, or RF communication.

[0013] The quadcopter itself consists of a lightweight frame equipped with four brushless DC motors, electronic speed controllers (ESCs), and a flight control system based on an Arduino Uno or Arduino Nano. The flight controller is connected to a gyroscope and accelerometer module, typically an MPU6050, which monitors angular velocity and linear acceleration in three axes. This sensor data is used in a closed-loop feedback system to dynamically adjust the motor speeds and maintain balance during flight. The stabilization logic is implemented in the microcontroller firmware. The MPU6050 sensor readings are filtered with complementary or Kalman filters to remove noise and drift. The output values ​​are compared to thresholds to detect changes in pitch, roll, and yaw.A PID (proportional-integral-derivative) controller is optionally implemented to ensure smoother adjustments and correct deviations from the desired orientation. The quadcopter receives gesture commands and executes them by mapping them to PWM (pulse-width modulation) signals for each motor. For example, an upward movement increases the throttle evenly across all four motors, while a downward movement to the left decreases the speed of the left motors and increases the speed of the right motors to trigger a roll. The quadcopter is powered by a rechargeable lithium-polymer battery.

[0014] A voltage regulator ensures a stable power supply to sensitive components such as the Arduino board and the IMU sensor. The system includes basic safety features such as manual override of the shutdown switch, signal timeout detection, and gesture confirmation delay to prevent accidental activation. The system architecture is modular and scalable. The gesture recognition algorithm can be updated to support additional gestures, and the communication interface can be upgraded to Wi-Fi or ESP-NOW to increase range and reduce latency. The on-board control logic can also be expanded with GPS modules for autonomous navigation or ultrasonic sensors for obstacle avoidance. In a representative implementation, the system successfully recognized five different hand gestures and mapped them to six drone commands.The drone remained stable within a range of ±5 degrees in moderate wind and responded to gesture changes with a latency of less than 300 milliseconds. The invention offers a cost-effective, reproducible, and open-source alternative to commercial gesture-controlled drones. Its intuitive control mechanism and easy integration make it suitable for educational institutions, UAV beginners, remote sensing, and assistive robots for users with physical disabilities.

Claims

[1] System for gesture-controlled automatic stabilization and navigation of a quadcopter using Arduino and computer vision, consisting of: A gesture recognition unit includes a camera configured to capture real-time video of a user's hand. a processing unit operatively connected to the camera, the processing unit configured to execute image processing and gesture classification algorithms to recognize predefined hand gestures and generate corresponding flight control commands. a wireless communication interface configured to transmit the flight control commands from the processing unit to the quadcopter. a quadcopter that includes: a frame structure with four engines and propellers arranged in a cross configuration; a microcontroller-based flight controller mounted on the frame; a sensor module that uses a gyroscope and an accelerometer configured to detect pitch, roll, and yaw; and stabilization control logic executed by the flight controller, the logic configured to process sensor data in real time and dynamically adjust motor speeds to maintain the orientation of the quadcopter; wherein the flight controller is further configured to receive the flight control commands from the gesture recognition unit and map the commands to motor control signals for directional flight, The system enables the quadcopter to navigate and stabilize in real time based on contactless hand gestures, without the need for a conventional remote control or wearable device. [2] The system of claim 1, wherein the gesture classification algorithm is implemented using rule-based logic or a machine learning model trained to recognize at least five different hand gestures. [3] The system of claim 1, wherein the camera is configured to operate at a frame rate between 15 and 30 frames per second and is positioned to capture the user's hand within a fixed interaction zone. [4] The system of claim 1, wherein the wireless communication interface is selected from Bluetooth, RF (radio frequency), Wi-Fi or ESP-NOW protocols. [5] The system of claim 1, wherein the stabilization control logic comprises a proportional-integral-derivative (PID) controller configured to compensate for drift and external disturbances during flight. [6] The system of claim 1, wherein the sensor module comprises an inertial measurement unit MPU6050 for detecting real-time changes in angular velocity and acceleration. [7] The system of claim 1, wherein the microcontroller-based flight control is implemented using an Arduino Uno, Arduino Nano or a comparable open source hardware platform. [8] The system of claim 1, wherein the motor control signals are generated using pulse width modulation (PWM) to drive four electronic speed controllers (ESCs) connected to brushless DC motors.