QuadX Drone

1. Context Overview

Industry

Robotics & Aerospace

Team

Scope

This project aims to design, build, and test a functional drone using the Quadrino Nano Flight Controller, with the ultimate goal of creating a versatile aerial platform. The first phase focuses on building and tuning a robust drone capable of stable flight, while the second phase incorporates computer vision and marker-based tracking inspired by pennsylvania university labs. The project is guided by the constraints of cost, material availability at the university lab, and the practicality of components.

Time

WIP

Goal

To build a functional and efficient flight controller that ensures stable flight and enables autonomous tasks for drones.

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2. Product Overview

Problem

The challenge is to design and build a stable, cost-effective drone using the Quadrino Nano Flight Controller. The project must account for:

• Limited material availability and budget constraints.
• Accurate calibration and tuning for stable flight.
• Extending drone functionality to include computer vision-based tracking and hovering.

Approach

1. Material Selection:

• Based on availability in the university lab and cost efficiency.
• Components include frame, motors, ESCs, Quadrino Nano, and LiPo battery.

2. Software and Hardware Integration:

• Configure and calibrate the Quadrino Nano using its GUI.
• Program and test control algorithms, focusing on stability and maneuverability.

3. Mathematical Modeling and Simulation:

• Use MATLAB and Simulink for drone dynamics and PID control simulation.

4. Testing and Validation:

• Iterative PID calibration and real-world testing.
• Analysis of performance under different scenarios.

5. Future Enhancements:

• Integrate computer vision for tracking and hovering using IR cameras and markers.

Solution

• A functional QuadX drone designed using:
  • Quadrino Nano Flight Controller.
  • Optimized components (motors, ESCs, and frame).
• Calibration and control tuning:
  • PID gains refined via software and real-world testing.
• MATLAB and Simulink simulation to predict and optimize performance.

Results

Phase 1:

• Stable and efficient drone capable of basic hovering and maneuverability.
• Demonstrated proper integration of hardware and software.

Phase 2 (Planned):

• Implementing a computer vision system for tracking and hovering.
• Leveraging inexpensive IR cameras and markers for practical applications.

Validation

• Achieved consistent stability with tuned PID.
• Flight time and power consumption met design expectations.

3. Process Overview

Research:

• Studied drone kinematics, control algorithms, and flight dynamics.
• Analyzed existing open-source solutions like ArduPilot and PX4.

Choice of Materials:

1. Frame

• Choice: Crazy2Fly Frame.

  

• Reasoning: Balancing strength and weight for better flight efficiency.

• About the Crazy2Fly: The Crazy2Fly multi-rotor UAV is a smaller, high performance quadcopter. The Crazy2Fly can be flown in either ’+’ or ‘x’ configurations. The Crazy2Fly is smaller than the VTail and considerably easier to understand and fly. Don’t let its simplicity and low cost fool you though - the Crazy2Fly lives by its name, and is capable of high speed aerial acrobatics and is a thrill to fly.

• The Mechanics: The frame of the Crazy2Fly uses G10 fiberglass composite which is incredibly rigid and lightweight and offers significant price advantages over carbon fibre. The hardware is entirely metal, using lightweight aluminium standoffs, steel screws and lock nuts.

2. Motors

• Choice: T-Motor Navigator MN3110 470KV
• Reasoning: High efficiency and power-to-weight ratio, necessary for stable flight and maneuverability.

3. ESCs (Electronic Speed Controllers)

• Type: Lynxmotion 12A Multirotor ESC 1A BEC.
• Reasoning: Smooth motor control for stability during hovering and maneuvers.

4. Flight Controller

• Choice: Quadrino Nano Flight Controller.
• Reasoning: Feature-rich, cost-effective, and suitable for our educational and research objectives.
• Capabilities: PID tuning, gyro stabilization, and flexibility for adding computer vision in phase 2.
• The Lynxmotion Quadrino Nano was created as a collaboration between FlyingEinstein and Lynxmotion. It was designed to be one of the smallest MultiWii compatible flight controllers on the market. The board includes many additional features normally reserved to much more expensive flight controllers, such as cutting-edge sensors, integrated GPS, easy to use software and a case with vibration damping.

5. Controller Radio

• Choice: FLYSKY FS-i6 Transmitter & Receiver

• Reasoning: Budget-friendly option with sufficient channels for drone control.

• FS-i6 Transmitter

  
  • The main advantage of this transmitter is that we don’t need any PC or laptop to set up this transmitter. It has one LCD display on it and we can easily set up this transmitter by using the buttons given on it. It can operate up to 1500 meters. The range of transmitters depends on magnetic interference. If the magnetic interference is more at someplace, then the transmitter will have less range and if the magnetic interference is less, then the transmitter will operate at a higher range.

• FS-i6 Receiver

  
  • This receiver has 2 antennas and 6 channels. For the best quality signal, the receiver should be mounted away from the motors or metal parts. The connectors are used to connect the parts of the model and the receiver.
    • CH1 to CH6: These channels are used to connect the ESCs (Electronic speed controller), Vcc, or other parts.
    • B/VCC: It is used to connect the binding cable for the binding receiver and transmitter.

6. Battery

• Choice: Lipo 3S 4000mah 11.1V 60C.
• Reasoning: Optimal balance between weight and power for flight time and stability.

Power Consumption and Thrust to Weight ratio

• Thrust-to-Weight:

  • Component Weights:

    • Crazy2Fly Frame: 250g
    • Motor (MN3110 470KV Brushless Motor): 98g (for 1 motor)
    • LiPo 3S Battery: 300g
    • 9.4” x 5” Propeller: Estimated 35g for 1 propeller
    • Quadrino Nano Flight Controller: Estimated 15g
    • Payload (Variable x): x grams

  • Total Weight (without Payload)

    Total Weight (without Payload) = Frame + (4 × Motor) + Battery + (4 × Propeller) + Flight Controller
= 250 + (4 × 98) + 300 + (4 × 35) + 15
= 250 + 392 + 300 + 140 + 15 = 1097 grams (without Payload)

  • Total Weight (with Payload):

    Total Weight (with Payload) = 1097 + x grams (where x is the payload)

  • MAX Thrust

    Total Thrust = 4 × 1200 = 4800 grams of total thrust

  • Thrust-to-Weight Ratio:

    Thrust-to-Weight Ratio = Total Thrust / Total Weight (with Payload)
= 4800 / (1097 + x)

  • Final Formula for Thrust-to-Weight Ratio:

    Formula:
    Thrust-to-Weight Ratio = 4800 / (1097 + x)

  • Table Summary:

    Component	Weight (grams)
    Crazy2Fly Frame	250
    Motor (MN3110 470KV)	98 (each)
    LiPo 3S Battery	300
    9.4” x 5” Propeller	35 (each)
    Quadrino Nano Flight Controller	15
    Payload (Variable x)	x
    Total Weight (Without Payload)	1097
    Total Weight (With Payload)	1097 + x
    Total Thrust	4800
    Thrust-to-Weight Ratio	4800 / (1097 + x)

• Power Consumption

Component	Power Consumption (Estimated)
Crazy2Fly Frame	Negligible (frame does not consume power directly)
Motor (MN3110 470KV) - Each	222W
LiPo 3S Battery	Voltage = 11.1V (Capacity = 3000mAh assumed)
9.4” x 5” Propeller	Varies based on motor load
Quadrino Nano Flight Controller	10W
Payload (Variable x)	Increases power consumption by 0.2W per gram of payload
Total Power Consumption (Without Payload)	898W
Total Power Consumption (With Payload)	898 + 0.2x W

Software and Coding :

1. Firmware Config Tool

• To reconfigure and reflash the firmware on the Quadrino board, we utilized the Firmware Config Tool designed specifically for this product. This user-friendly, wizard-like application guided us through the entire process, providing detailed documentation on the various options and parameters available. After selecting our desired configurations, the tool compiled and uploaded the custom firmware directly to the Quadrino board. Additionally, it conveniently installed the MultiWiiConf configuration GUI, streamlining the setup process and ensuring a seamless experience.

2. Sensor Calibration

• Gyro and accelerometer calibration performed via software tool.
• Ensured accurate data for stabilization algorithms.

3. Code for Control

• Language: C++ based config tool for more calibration.
• Implementation: Adjustments made for stability and initial PID values.

Mathematical Modeling and Simulation

1D Model

The dynamic equation for the motion of the quadrotor in the z-direction is:

⁢ż̇ = u / m - g

• Where:
  • ⁢ż̇: Acceleration in the z-direction
  • u: Total thrust generated by the quadrotor
  • m: Mass of the quadrotor
  • g: Gravitational acceleration

2D Model

In the planar model of the quadrotor, we only consider the thrust force on two of the rotors. The quadrotor has two inputs:

• Thrust Force (u₁): This is the sum of the thrusts generated by each rotor.

u₁ = Σ_i=1² F_i

• Moment (u₂): This is proportional to the difference between the thrusts of the two rotors.

u₂ = L(F₁ - F₂)

1. Drone Dynamics in MATLAB

• 1D Quadrotor Control
  • Following a line trajectory
  • Considered the thrust force in the Z Plane
  • PD controller

• 2D Quadrotor Control
• Following a sine wave trajectory
• Considered the thrust force in the Y-Z Plane
• PD controller

• 3D Controller Quadrotor
• Following a line trajectory
• PID controller
• Position and Attitude Control

2. PID Control in Simulink

• Link to a scientific article of Imad-Naciri.

Calibration and Testing

1. PID Calibration

• Calibrated PID parameters through the Quadrino Nano GUI.
• Iteratively adjusted gains for roll, pitch, and yaw stabilization.

2. Testing Process

• Initial tests involved hovering and simple maneuvers.
• Analyzed response to control inputs and made adjustments to PID gains.

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4. Experience Overview

Challenges:

• Synchronizing sensor data in real time.
• Achieving stable flight under varying environmental conditions.

Learning:

• Gained in-depth knowledge of drone dynamics and control systems.
• Improved problem-solving skills in hardware and software integration.

Impact:

• Created a low-cost, customizable flight controller suitable for research and education.
• Demonstrated successful drone navigation and autonomous behavior.

Future:

1. Phase 2: Computer Vision and Marker-Based Tracking

• Implement a vision system using inexpensive equipment (e.g., IR cameras and markers).
• Program the drone to track objects, hover at specified positions, and perform autonomous tasks.

2. Expand Applications

• Explore applications in education, research, and autonomous navigation.
• Use the platform to test innovative control algorithms.

3. Advanced Control Techniques

• Investigate model predictive control or adaptive control for enhanced stability and performance.

Conclusion

This project showcases a cost-effective approach to building a functional drone with extensive capabilities. By leveraging available resources and focusing on innovative enhancements in the second phase, the project highlights practical applications of robotics, control theory, and computer vision.