Hi, we are KufiyaTech team , representing our beloved country , Palestine!

    

This repository contains engineering materials of a self-driven vehicle's model participating in the WRO Future Engineers competition in the season 2024.

Content

• t-photos contains 2 photos of the team (an official one and one funny photo with all team members)
• v-photos contains 6 photos of the vehicle (from every side, from top and bottom)
• video contains the video.md file with the link to a video where driving demonstration exists
• schemes contains one or several schematic diagrams in form of JPEG, PNG or PDF of the electromechanical components illustrating all the elements (electronic components and motors) used in the vehicle and how they connect to each other.
• src contains code of control software for all components which were programmed to participate in the competition
• models is for the files for models used by 3D printers, laser cutting machines and CNC machines to produce the vehicle elements. If there is nothing to add to this location, the directory can be removed.
• other is for other files which can be used to understand how to prepare the vehicle for the competition. It may include documentation how to connect to a SBC/SBM and upload files there, datasets, hardware specifications, communication protocols descriptions etc. If there is nothing to add to this location, the directory can be removed.

READ.ME table of content

1. Project Overview

   1.1. Competition Context

   1.2. Team Management

   1.3. Strengths and Limitations of Our Vehicle

2. Programming Language and Libraries

   2.1. Programming Language and Libraries

   2.2. Detecting Turns and Direction

   2.3. Lap Counting Method

   2.4. IMU-Based Steering

3. Open Challenge

   3.1. Open Challenge Overview

   3.2. PID Controller

   3.3. Turn Execution

4. Obstacle Avoidance Round Challenge

   4.1. Obstacle Challenge Overview

   4.2. Pillar Detection

   4.3. Obstacle Avoidance Strategy

5. Designing Process

   5.1. Steering System

   5.2. Differential Gear

   5.3. Chassis

   5.4. Mechanism

   5.5. Ackermann Steering Mechanism

6. Power and Sense Management

   6.1. Power Source

   6.2. Sensors We Used and Their Functions

7. Hurdles and Challenges

   7.1. Designing Process Challenges

   7.2. Sensor and Coding Challenges

   7.3. Mechanical Challenges

8. Future Work

9. Appendices

   9.1. Datasheets and Specifications

   9.2. References

10. Reflections and Gratitude

Project Overview

This project has been in development for nearly three months to participate in the WRO Future Engineers 2024 competition. The competition challenges teams to develop a self-driving car capable of autonomously completing two rounds on a designated track. You can view the competition track here.

A self-driving car is a vehicle designed to operate with minimal to no human intervention. For this project, our focus has been on key areas such as sensor fusion, computer vision, and advanced control systems, which are critical to the vehicle's performance.

The project is aimed at engineering students, robotics enthusiasts, and anyone interested in autonomous vehicle technologies, especially those within the 14-19 age group. It incorporates a range of technologies, including C++, Arduino, and AI cameras for computer vision tasks.

The vehicle’s architecture features an Arduino-based control system, integrated with various sensors such as an IMU, ultrasonic sensors, and a camera. These components work together to achieve the desired level of autonomy.

To meet the competition’s demands, our approach integrates sophisticated hardware and software solutions. We’ve developed robust algorithms that enable the car to make autonomous decisions based on real-time sensor data. This not only demonstrates the car’s ability to avoid obstacles but also highlights its precision in following designated paths. The project spans multiple engineering disciplines, including electronics, mechanics, and software development, making it a comprehensive showcase of modern engineering applied to robotics.

1.1 Competition Context

The WRO Future Engineers 2024 competition challenges teams to design and develop autonomous vehicles capable of navigating a predefined course while executing specific tasks. This competition is structured across multiple rounds, including a practice round, two qualification rounds, and a final round, each progressively testing the vehicle's capabilities.

Participants must engineer their vehicles to handle complex challenges such as lane following, obstacle detection, and precise turning. The competition track is designed with a variety of elements, including straight paths, curves, and intersections, which the vehicle must navigate autonomously without human intervention.

Scoring is based on the vehicle's ability to complete the course with accuracy and efficiency, with additional points awarded for successful task execution and speed.

For detailed competition rules, please refer to the official WRO Future Engineers 2024 guidelines: WRO Future Engineers 2024 Rules

1.2 Team Management

Our project is the result of a collaborative effort by a skilled and dedicated team, each member bringing unique expertise to the table. We are students of Purpose Robotics Academy, located in Ramallah, Palestine, where we have gained valuable skills in robotics and technology. You can learn more about the academy through their website and Facebook page.

• Sara Jawaada: As the lead designer, Sara was instrumental in overseeing the entire design process, ensuring that the physical and conceptual elements of the project were seamlessly integrated. Additionally, she played a key role in software development, contributing to both the architecture and implementation of core functionalities.

  Contact Information:
  Email: sarajawaada906@gmail.com

• Amro Duhaidi: Amro led the technical implementation of the software, with a particular focus on sensor integration and system wiring. His expertise in coding and hardware interfacing was crucial in developing the responsive and reliable behavior of the robot, ensuring that all sensors were effectively utilized in the project's logic.

  Contact Information:
  Email: amro.duaidi@gmail.com

Project Supervisor

We would like to extend our deepest gratitude to our mentor and coach, Eng. Mohammad Muamar. A distinguished engineer and graduate of Palestine Polytechnic University in Hebron, Eng. Muamar has been instrumental in guiding our team through every step of this competition. His dedication, expertise, and unwavering support have been invaluable to our progress and success.

As students of esteemed professors who are proud holders of Palestinian degrees with expertise of global standards, we are honored to have Eng. Muamar as our mentor. His commitment to excellence and his belief in our potential have profoundly shaped our approach and achievements in this project.

Contact Information:

• Email: moh.mummar@gmail.com

KufiyaTech Team's Social Accounts

Stay connected with our team and follow our journey through our official social media channels. Explore more about our work, updates, and behind-the-scenes moments by visiting our link tree:

• KufiyaTech Social Accounts: https://linktr.ee/kufiyatech

1.3. Vehicle Strengths and Limitations

Strengths:

• Chassis and Mechanism: We designed the chassis ourselves and incorporated a mechanism built using the LEGO kit. This approach not only ensures that the vehicle meets the competition’s specific requirements but also provides us with valuable hands-on experience in both design and engineering.

• Lightweight and Fast

Limitations:

• Size

• Mechanism Precision

Vehicle Dimensions:

-Vehicle weight: 980 gm

• Without Camera:

  • Length: 26 cm
  • Width: 16.7 cm
  • Height: 10.5 cm

• With Camera:

  • Length: 23 cm
  • Width: 16.7 cm
  • Height: 18 cm

2. Programming Language and Libraries

Programming Language: C++

• Why C++?:

• We chose C++ because it is well-supported by the Arduino IDE and provides robust library support, making it an ideal choice for our project.

We leveraged several libraries to implement the functionalities required for our robot:

• Wire.h: Facilitates I2C communication between the Arduino and various sensors, such as the IMU, allowing us to efficiently gather and process sensor data.

• Servo.h: Used for controlling the servo motors, particularly in steering and managing precise movements.

• Pixy2.h: Handles communication with the Pixy2 camera sensor, which is crucial for visual tracking and object recognition tasks.

• MPU6050.h: Provides functions to interface with the MPU6050 IMU sensor, enabling accurate detection of orientation, acceleration, and gyroscopic data to manage the robot's movement and stability.

These libraries, combined with the robustness of C++, provide the necessary tools to implement the complex control logic required by our autonomous vehicle.

2.4 Detecting Turns and Direction

Problem Statement: In the WRO Future Engineers competition, the direction of the car's movement is not predetermined; it can be either clockwise or counterclockwise. This unpredictability introduces an additional layer of complexity, particularly when the car needs to execute precise U-turns at specific points on the track. The challenge is to accurately detect the required direction and execute the turn with precision.

First Solution: Using the TCS3200 Color Sensor

One approach to solving this problem involved the use of the TCS3200 color sensor to detect the color-coded lines on the track that indicate the direction of the car's movement.

• Color Detection: The TCS3200 sensor was programmed to recognize specific colors on the track. An orange line signals that the car should move in a clockwise direction, while a blue line indicates a counterclockwise movement.

• Controlled Turning: Upon detecting a color, the car was instructed to turn at a specific angle. The turning angle was precisely controlled using an Inertial Measurement Unit (IMU), ensuring that the turn was executed accurately and consistently, regardless of external factors like speed or track conditions.

• Code Implementation: The process was automated through predefined functions such as detectOrangeLine and detectBlueLine, which triggered the appropriate turning actions based on the color detected.

This method provided a clear and direct way to determine the car's direction based on visual cues from the track, leveraging the IMU for precision in turning.

Second Solution: Using Three Ultrasonic Sensors

An alternative approach utilized three ultrasonic sensors to determine the direction of the car's movement based on distance measurements:

• Direction Determination: The ultrasonic sensors were positioned to monitor distances on the left, right, and front of the car. When the car reached a point where it needed to turn, the direction was determined by comparing the distances detected by the sensors:

  • If the distance measured by the right sensor was greater than that of the left sensor, the car would turn clockwise.
  • Conversely, if the left distance was greater, the car would turn counterclockwise.

• Front Sensor Trigger: Additionally, the front ultrasonic sensor played a crucial role by detecting when the car was approximately 50 cm away from an obstacle or turning point. This detection served as a cue for the car to initiate the turn.

This method relied on spatial awareness and distance measurement, allowing the car to make decisions based on its surroundings without relying on visual markers.

Figure 1: Illustration of the turn detection logic using ultrasonic sensors.

As illustrated above, when the car reaches the lines where it needs to turn, the direction of the turn is determined by the distance sensors. If the sensors detect that the right distance is greater than the left distance, the car should turn clockwise. If the left distance is greater than the right distance, the car will move counterclockwise. When the front sensor detects that the distance is approximately 50 cm, it indicates that the car should turn.

Final Decision: Implementing the Ultrasonic Sensor-Based Approach

After careful consideration, the "Kufiya" team decided to implement the second method using ultrasonic sensors. This decision was driven by numerous challenges encountered with the first method involving the color sensor. Despite spending two months attempting to fine-tune the color sensor, we faced significant issues with its accuracy, particularly in varying lighting conditions and with precise color recognition.

The sensor's inconsistent performance led to unreliable direction detection, which was critical for the success of our project. These difficulties prompted us to shift our focus to the ultrasonic sensor-based approach, which provided a more reliable and adaptable solution for determining the car's movement direction and executing precise turns.

2.5 Lap Counting Method

Problem Statement: The goal is to implement a lap counting mechanism for an autonomous vehicle (or RC car) that accurately counts the number of laps the vehicle completes on a designated track. A key requirement is that after completing exactly three laps, the vehicle must automatically stop.

1. Color Sensor-Based Detection

Overview: The first method involves utilizing a color sensor (TCS3200) to detect specific markers placed on the track. This technique leverages the sensor's ability to identify distinct colors, allowing it to register a lap each time the sensor passes over a designated colored marker.

Principle:

• The color sensor is calibrated to recognize a specific color distinct from the track's surface.
• When the sensor detects this color, it triggers a signal to increment the lap count.

Why Do We Not Recommend This Method?

• Accuracy Issues: Despite extensive efforts, the color sensor consistently struggled to provide accurate readings, particularly with the color orange. Ambient lighting conditions significantly affected the sensor's performance, even with a custom shield.
• Programming Challenges: Programming the sensor to reliably identify the target color was difficult, especially given the high speed of the vehicle, which impaired the sensor’s ability to detect and register the color in real-time.
• Unreliable Performance: The sensor's inconsistent and imprecise measurements, especially under dynamic conditions, made it an unsuitable choice for our project. This unreliability led us to explore alternative lap counting methods.

2.5 Loop-Based Counting with Ultrasonic Sensor and IMU

Overview: The second method utilizes a software-based loop counter that increments the lap count each time a specific condition within the code is met. In our approach, the robot detects the completion of a lap using an ultrasonic sensor. When the sensor detects that the distance to a specific point or object is less than 50 cm, the robot recognizes this as an indicator to begin a new lap. The robot’s steering is controlled by an IMU (Inertial Measurement Unit), which ensures precise navigation during turns.

Principle:

• The loop-based method is implemented by continuously monitoring the distance using the ultrasonic sensor.
• When the sensor detects that the distance has decreased below 50 cm, the robot initiates a turning maneuver, guided by the IMU.
• This detection and turning process is managed within a loop structure in the code, specifically within a while loop that executes as long as the distance remains below 60 cm.
• Each time this condition is met, the program increments the lap count, allowing for accurate tracking of the robot’s laps.

Advantages of This Method:

• Reliability: This method provides more reliable lap counting, unaffected by external factors like ambient light.
• Precision: The IMU ensures precise control of the vehicle’s steering, contributing to accurate lap counting

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2.6 IMU-Based Steering

Overview:

In our autonomous vehicle, precise steering control is essential for navigating the course accurately. We implemented an IMU-based steering mechanism to achieve this. The IMU (Inertial Measurement Unit) provides real-time data about the vehicle’s angular velocity, which is critical for calculating the yaw angle (the vehicle’s turning angle) and ensuring stable and accurate steering.

Understanding the IMU:

An IMU (Inertial Measurement Unit) is a sensor device that combines multiple sensing components, usually a 3-axis accelerometer and a 3-axis gyroscope. These sensors work together to track the vehicle's motion and orientation in three-dimensional space.

• Accelerometer: Measures the acceleration along the X, Y, and Z axes. It helps in determining the tilt of the vehicle.
• Gyroscope: Measures the angular velocity (the rate of rotation) around the X, Y, and Z axes. It is crucial for determining the vehicle's rotational motion, especially the yaw angle.

The X, Y, and Z Axes:

Understanding the axes is essential for interpreting the data provided by the IMU:

• X-Axis: Represents the roll, or the tilting motion of the vehicle from side to side.
• Y-Axis: Represents the pitch, or the tilting motion of the vehicle from front to back.
• Z-Axis: Represents the yaw, or the rotational movement of the vehicle around the vertical axis. This axis is crucial for steering, as it defines the direction the vehicle is facing.

*Figure 2: The X, Y, and Z axes relative to the vehicle.

Calculating the Yaw Angle:

The yaw angle ((\theta_z)) is a measure of the vehicle's rotation around the Z-axis. The gyroscope within the IMU provides angular velocity data along this axis, which tells us how quickly the vehicle is rotating.

To determine the vehicle's orientation or heading, we calculate the yaw angle by integrating the angular velocity over time. The yaw angle changes based on the rate of rotation, and by continuously updating this value, we can keep track of the vehicle's direction.

The equation used to calculate the yaw angle is:

Where:

• (theta_z) is the current yaw angle.
• (theta_{z, \text{previous}}) is the yaw angle from the previous time step.
• ({gyro}_z) is the angular velocity around the Z-axis (provided by the gyroscope).
• ({gyro_z_offset}) is the gyroscope offset, calculated during calibration to correct any drift.
• (Delta t) is the time elapsed between the current and previous readings.

Importance of Gyro Offset:

The gyroscope offset ((\text{gyro_z_offset})) is critical because gyroscopes can have slight errors or biases over time, known as drift. By calculating and subtracting this offset, we ensure that the yaw angle calculation is accurate and doesn't gradually deviate from the true value.

PID Control for Steering:

To maintain or correct the vehicle’s path, we implemented a PID (Proportional-Integral-Derivative) controller. The PID controller adjusts the steering angle based on the difference between the target yaw angle and the current yaw angle. This ensures smooth and stable steering.

3.Open Challenge

3.1 Open Challenge Overview

The vehicle must complete three (3) laps on the track with random placements of the inside track walls

Round objectives:

• Moving between the internal and external wall.

• Turning the car when detecting the lines .

• Detecting blue and orange lines on the mat.

• Counting the laps.

• The car stops after 3 laps.

Round constraints:

• Time.

• The car Turning in the correct angle.

• The direction of the car's movement is random.

• The position from which the car starts moving is random.

• The distance between the internal and external wall is random.

3.2 PID Controller

Overview:

The PID (Proportional-Integral-Derivative) controller is a widely used control loop feedback mechanism in industrial and automation systems. It is designed to continuously adjust a process to maintain the desired output, known as the setpoint, by minimizing the error between the setpoint and the actual process variable.

The PID controller operates by combining three distinct control actions:

1. Proportional (P): Reacts to the current error by producing an output that is proportional to the error. It provides immediate corrective action but can lead to overshoot if used alone.
2. Integral (I): Addresses accumulated past errors by summing them over time, helping to eliminate steady-state errors.
3. Derivative (D): Predicts future errors by considering the rate of change of the error, helping to reduce overshoot and oscillations.

The control signal, which is sent to the servo motor, is calculated as:

Figure 3: PID Control System for Steering.

Code Implementation:

The code implementation ties together the IMU readings, yaw angle calculation, and PID control to manage the vehicle's steering:

Key Equations:

Proportional Term:

Figure 4: The Proportional Term equation, where \( e(t) \) is the current error and \( K_p \) is the proportional gain.

Integral Term:

Figure 2: The Integral Term equation, which sums past errors over time, multiplied by the integral gain \( K_i \).

Here, integral is the accumulated error over time.

Derivative Term:

Figure 5: The Derivative Term equation, which considers the rate of change of the error, multiplied by the derivative gain \( K_d \).

Combined PID Control:

Figure 6: The Combined PID Control equation that sums all three components to generate the control output.

In our code:

• error is the difference between the target yaw angle (targetYawAngle) and the current yaw angle (yaw_angle).

• integral accumulates the error over time.

• derivative is the rate of change of the error.

• controlSignal is the final output applied to the servo motor, calculated using the combined PID equation.

Implementation in Our Project:

In our autonomous vehicle project, the PID controller is utilized to maintain the vehicle's yaw angle, ensuring that it follows the desired path accurately.

How It Works:

1. Initialization:

   • The vehicle's IMU (Inertial Measurement Unit) is initialized to provide real-time gyroscope data. This data is crucial for determining the yaw angle, which represents the vehicle's direction.

2. Error Calculation:

   • The yaw angle error is calculated by comparing the current yaw angle with the target yaw angle. This error is then used to compute the necessary adjustments.

     error = yaw_angle - targetYawAngle;

3. PID Calculations:

   • Proportional: The error is multiplied by the proportional gain ( K_p ) to determine the immediate correction.
   • Integral: The sum of past errors is calculated and multiplied by the integral gain ( K_i ), contributing to the correction by addressing accumulated deviations.
   • Derivative: The rate of change of the error is determined and multiplied by the derivative gain ( K_d ), providing a predictive adjustment to smooth out the response.

     integral += error * (currentTime - previousTime);
     derivative = (error - previousError) / (currentTime - previousTime);

4. Control Signal Generation:

   • The control signal, which adjusts the servo motor's angle, is computed as the sum of the proportional, integral, and derivative components:

     controlSignal = Kp * error + Ki * integral + Kd * derivative;
     float t = controlSignal + 90;
     t = constrain(t, 40, 140);
     myServo.write(int(t));

5. Practical Application:

   • As the vehicle moves, the PID controller continuously adjusts the steering to keep the vehicle on course. If the vehicle deviates, the controller calculates the necessary steering corrections to bring it back on track smoothly.

#include <Wire.h>
#include <MPU6050.h>
#include <Servo.h>

MPU6050 mpu;
Servo myServo;
int16_t gz;
float yaw_angle = 0, gyro_z_offset = 0;
float Kp = 2, Ki = 0.001, Kd = 0.5;
float error, integral = 0, derivative, controlSignal;
unsigned long previousTime, currentTime;

void setup() {
    Serial.begin(9600);
    Wire.begin();
    mpu.initialize();
    myServo.attach(SERVO_PIN);
    Serial.println("Calibrating gyro...");
    calculateGyroDrift();
    Serial.println("Done Calibrating");
    previousTime = millis();
}

void loop() {
    MoveFWwithGyro();
}

void MoveFWwithGyro() {
    currentTime = millis();
    float elapsed_time = (currentTime - previousTime) / 1000.0;
    previousTime = currentTime;

    gz = mpu.getRotationZ() - gyro_z_offset;
    float gyro_z = gz / 131.0;
    yaw_angle += gyro_z * elapsed_time;

    error = targetYawAngle - yaw_angle;
    integral += error * elapsed_time;
    derivative = (error - previousError) / elapsed_time;
    controlSignal = Kp * error + Ki * integral + Kd * derivative;
    controlSignal = constrain(controlSignal, 40, 140);
    myServo.write(int(controlSignal));

    previousError = error;
    Serial.print("Yaw Angle: "); Serial.println(yaw_angle);
}

void calculateGyroDrift() {
    for (int i = 0; i < 2000; i++) {
        gyro_z_offset += mpu.getRotationZ();
        delay(2);
    }
    gyro_z_offset /= 2000;
}

Explanation of the Code:

1. Setup:

   • The IMU and servo motor are initialized. The gyroscope is calibrated to calculate the gyro_z_offset.

2. Main Loop:

   • The function MoveFWwithGyro() is called continuously. It reads the gyroscope data, calculates the yaw angle, and then computes the control signal using the PID controller.

3. Yaw Angle Calculation:

   • The yaw angle is updated based on the angular velocity around the Z-axis, factoring in the time elapsed since the last update.

4. PID Control:

   • The error between the desired and actual yaw angle is calculated. The control signal, which adjusts the steering angle, is then determined by the PID formula. This control signal is sent to the servo motor to adjust the vehicle's direction.

Conclusion:

The PID controller is a critical component in our system, ensuring the vehicle maintains a stable and accurate trajectory. By balancing the immediate response (P), accumulated error correction (I), and predictive adjustment (D), the PID controller allows for precise control of the vehicle's movements, even in dynamic and challenging environments.

3.2 Turn Execution

To ensure the car turns efficiently when reaching U-turns, we applied precise control for accurate turn execution.

Turn Execution Logic

The process of executing turns integrates several key components to achieve precision and reliability:

1. Ultrasonic Sensors:

   • Our vehicle is equipped with ultrasonic sensors on the left, right, and front sides, which continuously provide distance measurements from walls.
   • These sensors play a crucial role in determining the need and direction of the turn by comparing the distances detected on either side of the vehicle.

2. Gyroscope (Inertial Measurement Unit - MPU6050):

   • The IMU sensor provides real-time data on the vehicle's yaw angle, which is the rotational angle around the vertical axis.
   • This data is essential for calculating the vehicle's current orientation and ensuring that turns are executed with high angular precision.

3. PID Controller:

   • To ensure smooth and controlled turning, a Proportional-Integral-Derivative (PID) controller is used.
   • The PID controller calculates the difference between the current yaw angle and the target yaw angle, adjusting the steering motor accordingly.
   • This control mechanism is critical in avoiding overshooting or undershooting the turn, maintaining the vehicle's intended path.

Code Implementation for Turn Execution

The following code snippet demonstrates the logic and functions used to manage turn execution:

void MoveFWwithGyro() {
    long current_time = millis();
    currentTime = millis();
    float elapsed_time = (current_time - previous_time) / 1000.0;
    previous_time = current_time;

    gz = mpu.getRotationZ();
    gz -= gyro_z_offset;
    float gyro_z = gz / 131.0;
    yaw_angle += gyro_z * elapsed_time;

    error = yaw_angle - targetYawAngle;
    integral += error * elapsed_time;
    derivative = (error - previousError) / elapsed_time;
    controlSignal = Kp * error + Ki * integral + Kd * derivative;
    float t = controlSignal + 90;

    t = constrain(t, 50, 130);
    myServo.write(int(t));

    previousError = error;
    previousTime = currentTime;

    Serial.print("Yaw Angle: "); Serial.println(yaw_angle);
}

void TurnRight() {
    targetYawAngle -= 89; // Adjust this value for sharper or softer turns
    while (yaw_angle > targetYawAngle + 5) {
        MoveFWwithGyro();
    }
}

void TurnLeft() {
    targetYawAngle += 89; // Adjust this value for sharper or softer turns
    while (yaw_angle < targetYawAngle - 5) {
        MoveFWwithGyro();
    }
}

Overview of Operations

Turn Initiation:

• The robot initiates a turn when the track requires a change in direction, such as a U-turn.
• The turn direction is determined by comparing the distance readings from the left and right sensors, ensuring that the vehicle turns in the optimal direction.

Turn Execution:

• The MoveFWwithGyro() function is the core routine that continuously monitors the vehicle's orientation and adjusts the steering angle during the turn.
• Depending on the required turn direction, the TurnRight() or TurnLeft() function sets the target yaw angle and engages the PID-controlled steering adjustments until the vehicle reaches the desired orientation.
• The PID controller plays a critical role in minimizing error and stabilizing the vehicle's movement during and after the turn.

Post-Turn Adjustment:

• Once the vehicle achieves the required yaw angle, the steering mechanism returns the vehicle to a straight path, allowing it to continue its course efficiently.
• The combination of real-time sensor data and the PID control algorithm ensures that the vehicle resumes its path without deviation or drift.

By integrating advanced sensor data and control algorithms, our vehicle is capable of executing turns with high precision, a crucial capability for successfully navigating the competition track.

4. Obstacle Avoidance Round Challenge

Obstacle Challenge Overview

In this challenge, the autonomous vehicle must complete three laps on a track where green and red traffic signs are randomly placed. These signs instruct the vehicle on which side of the lane it should follow:

• Red Pillar: Indicates that the vehicle must stay on the right side of the lane.
• Green Pillar: Indicates that the vehicle must stay on the left side of the lane.

At the end of the second lap, the final traffic sign will determine the direction for the third lap:

• Green Traffic Sign: The vehicle continues the third lap in the same direction.
• Red Traffic Sign: The vehicle must turn around and complete the third lap in the opposite direction.

The vehicle is required not to move any of the traffic signs during the course. After completing all three laps, the vehicle must find the designated parking area and perform parallel parking.

Important Note: The initial driving direction (clockwise or counterclockwise) will change in different rounds of the challenge. Both the starting position of the vehicle and the number and location of traffic signs are randomly set before each round begins.

Round Objectives

• Traffic Sign Response:

  • Red Pillar: Steer right.
  • Green Pillar: Steer left.

• Third Lap Direction:

  • Green Sign: Continue in the same direction.
  • Red Sign: Reverse direction.

• Turn Execution:

  • Turn when reaching the U-TURN.

• Lane Keeping:

  • Stay centered between the walls.

• Final Task:

  • Perform parallel parking after completing three laps.

Round constraints:

• Randomized Pillar Placement
• Randomized Pillar Count
• Randomized Driving Direction
• Randomized Starting Position
• Randomized Parking Lot Location .

4.2 Pillar detection

Pixy2 Camera Overview

Pixy2 is a compact, fast, and versatile vision sensor specifically designed for robotics applications. Like its predecessor, Pixy2 can quickly learn to detect objects that you teach it, simply by pressing a button. It also features advanced algorithms for detecting and tracking lines, which is particularly useful for line-following robots. These algorithms can even detect intersections and recognize "road signs" that can instruct your robot to perform actions like turning left, turning right, or slowing down. All of this is processed at 60 frames-per-second, ensuring that your robot can operate at high speeds while still accurately detecting its environment.

For more details, you can visit the Pixy2 Official Website and check out this informative video on how to use Pixy2. Additionally, if you want to learn more about teaching Pixy2 to recognize objects, you can refer to the Pixy2 Documentation.

Example of Pixy2 in Action

We configured the Pixy2 camera using the PixyMon application to detect green and red pillars. Below are images captured from the PixyMon interface, showing the successful detection of these objects:

Figure 7: Pixy2 camera detecting a green object.

Figure 8: Pixy2 camera detecting a red object.

4.3 Obstacle Avoidance Strategy

In the Obstacle Avoidance Round, the vehicle employs the Pixy2 camera to detect pillars on the track. The logic behind the code ensures that the vehicle reacts appropriately based on the detected pillars’ size and color:

• Pillar Detection: The Pixy2 camera continuously scans for pillars along the vehicle’s path. Once a pillar is detected, the system measures its size to determine if it is relevant for further action.

• Size Check: If the detected pillar's size is greater than 1000, the vehicle proceeds to analyze its color. This size threshold helps filter out irrelevant objects or distant pillars that do not require an immediate response.

• Color-Based Reaction:

  • Green Pillar: If the pillar is green, the vehicle is programmed to make a left turn. This is achieved by adjusting the servo motor's angle to steer the vehicle in the correct direction.
  • Red Pillar: Conversely, if the pillar is red, the vehicle makes a right turn by similarly adjusting the servo motor's angle.

This process ensures that the vehicle navigates the track effectively by responding to the obstacles in its path with precise and controlled movements.

5. Designing Process

Building an RC car for the WRO Future Engineers competition from scratch has been a significant challenge, especially since it was our first time designing a robot. Although using a kit would have been an easier option, we were committed to building our own robot, which left us with no other options. As we began gathering the necessary components, our focus was on identifying the exact mechanisms required for our RC car. Our goal was to solve the challenges presented in the two competition rounds as effectively and simply as possible. The design of our robot has evolved over time, and here is how our designs have developed throughout this journey:

5.1 Steering System

• First Design:
  • Our initial steering system was functional, but we faced challenges in supporting and securing all the components on top of it.

Figure 9: First Steering System Design

• Second Design:

  • This design proved to be a better choice initially, but it was based on ready-to-print files sourced from the internet. This presented a dilemma: adhere to the predefined dimensions that we couldn't alter, or undertake the challenge of designing it entirely from scratch.
  • Link 1: 3D Printed RC Car with Brushless Motor

• Third Design:

  • Unlike our previous designs, this iteration did not require 3D printing because we utilized the EV3 LEGO kit for the mechanism. This approach saved us time and effort, providing the flexibility to modify and control it easily. The LEGO kit offered a reliable steering solution that we could test quickly, contrasting with the longer process required by our previous designs.

5.2 Differential Gear System

• First Design:

  • Our initial choice was similar to the second steering system option in terms of using ready-to-print files. However, we encountered familiar challenges, compounded by an additional difficulty: the inability to locate suitable shafts or bearings within our allotted timeframe.

  • Link 2: 3D Printed RC Car with Brushless Motor

• Second Design:

  • In this design, we chose to utilize the EV3 LEGO system to construct our steering mechanism, marking a departure from our previous approaches. This choice offered the same advantages as our third steering system design, particularly in terms of ease of management and reliability.

5.3 Chassis Design Process

• First Design:

  • Our initial design phase was the most time-consuming. Being our first attempt, we struggled with defining the shape of our robot and determining how it would accommodate all intended components, including five ultrasonic sensors. This constrained our design options significantly. Our primary objective was to create a chassis capable of housing all components, prioritizing functionality over minimizing size. We positioned the camera at the rear without adhering to standard engineering principles. Initially, we planned to use a 3D printer for manufacturing. -

    

  • Figure 11: First Chassis Design

• Second Design:

  • This was the first design we brought to life by printing it using a 3D printer. This design incorporated our chosen mechanism, but upon completion, we discovered it was too compact to meet our established standards.

• Figure 12: Second Chassis Design

Third Design:

• Before proceeding with the computer-aided design, we sketched our third design on paper. We then crafted a prototype using compressed cork to evaluate its dimensions. Our aim was to slightly increase its size while accommodating our chosen mechanisms. However, due to limitations with our printer, we were compelled to design two separate bases (chassis) and join them together. During testing, we discovered a potential vulnerability: the structure could potentially break apart under specific weight conditions.

Figure 13: Third Chassis Design - Front and Side Views

• Fourth Design:

  • We decided to shift from using a 3D printer to a CNC machine for bringing our designs to reality. This allowed us to create a single, connected chassis capable of supporting more weight. Our design continued to evolve due to changes in our mechanism and component choices. A significant change was opting for smaller LEGO wheels to improve steering, which took some time to perfect. Another challenge we faced was managing space constraints relative to the size of our mechanisms. However, with the guidance and support of our coach, we were able to find better solutions and overcome these obstacles. After deciding to use a CNC machine, we encountered several challenges in selecting the ideal material to meet our needs. Initially, we chose acrylic, but it proved too brittle and broke easily. We then selected wood, which met our goals and requirements perfectly.

  • If you are interested in viewing the detailed designs, please check the models directory in the repository.

5.4 Mechanism

• Rear-Wheel Drive (RWD):
  • Our vehicle’s drivetrain works with the engine to deliver power to the wheels. We utilized a rear-wheel drive system, where the engine's power is directed to the rear wheels. This system provided balanced weight distribution and higher control over the vehicle. The rear-wheel drive system consisted of several main components: the drive shaft, the rear axle, and the differential gears, which in turn transmitted the power to the axles and then to the wheels. Our car's mechanism was constructed using the LEGO EV3 kit.

5.5 Ackermann Steering Mechanism

• We used the Ackermann steering geometry, a configuration designed to ensure that the wheels of a vehicle trace out circles with different radii during a turn, preventing tire slippage. This geometry helps align the front wheels towards the turning center, providing improved handling and stability, especially at low speeds. It contrasts with other mechanisms like the Davis steering gear, offering simpler construction and fewer components susceptible to wear. Ackermann steering is commonly used in standard vehicles for its advantages in maneuverability and reduced tire wear.

• 

• Figure 14: Ackermann Steering Mechanism

6.Power and Sense management

6. Power and Sense Management

6.1 Power Source

Power Source: Why We Chose a 12V Lithium-ion Battery

Overview:

Lithium-ion batteries are a top choice in robotics due to their high energy density, lightweight design, and ability to deliver high currents. These features make them ideal for applications requiring both power and mobility, such as our autonomous robot.

Advantages of Lithium-ion Batteries:

1. High Energy Density: Lithium-ion batteries provide a high amount of energy in a small, lightweight package, which is critical for keeping the robot agile while providing enough power for all components.
2. High Discharge Rate: They can supply the high current needed to drive motors and other power-hungry components effectively.
3. Lightweight and Compact: Their design allows for easy integration into the robot without adding unnecessary bulk.
4. Stable Voltage Output: Ensures consistent performance of the robot's components, which is crucial for maintaining reliable operation.

Why a 12V Battery?

• Voltage Compatibility:

  • The 12V output is ideal for many robotics components, such as motors and motor drivers, which are designed to operate efficiently at this voltage. This minimizes the need for complex voltage conversion, simplifying the power distribution system.

• Power Sufficiency:

  • A 12V battery provides ample power for the entire robot, ensuring that high-demand components like motors receive enough voltage while still allowing for efficient voltage regulation down to 5V or 3.3V for other electronics.

Key Characteristics of the 12V Lithium-ion Battery:

• Voltage: 12V nominal, ideal for direct use with motors and for stepping down to lower voltages.
• Capacity: Typically 6600mAh, affecting how long the robot can operate before needing a recharge.
• Discharge Rate: High C-ratings (e.g., 20C) ensure the battery can supply the necessary current for motors and other components during peak usage.
• Weight: Lightweight design, typically around 150g for a 2200mAh battery, crucial for maintaining the robot’s agility.

Choosing the Perfect Power Supply for Your Robot:

When selecting a power supply or battery for your robot, consider the following factors:

• Energy Requirements: Assess the total power needs of your robot, including motors, sensors, and control units. Choose a battery that can provide sufficient voltage and current for all components.

• Battery Type: Lithium-ion batteries are often the best choice for robotics due to their high energy density and discharge rates. However, also consider other options like NiMH based on your specific requirements.

• Voltage and Capacity: Ensure the battery’s voltage matches the requirements of your highest voltage components, and its capacity (mAh) is sufficient to provide long operating times without frequent recharges.

• Weight and Size: The battery should be lightweight and compact to fit within your robot’s design without adding excessive weight that could affect performance.

• Safety Features: Choose batteries with built-in protection against overcharging, overheating, and short circuits to ensure the safety and longevity of your robot.

The 12V Lithium-ion battery was selected for our robot because it offers the best balance of power, efficiency, and weight. It provides the necessary voltage to power all components, from high-demand motors to sensitive electronics, ensuring the robot operates reliably and efficiently. By understanding the energy requirements and choosing a battery that meets these needs, we ensure that our robot remains powerful, agile, and safe.

Power Distribution from the 12V Lithium-ion Battery to Each Component

In our autonomous robot, the power distribution system is crucial to ensure that each component receives the correct voltage for optimal operation. The 12V Lithium-ion battery serves as the primary power source, and through a carefully designed distribution system, we step down and regulate the voltage to match the requirements of different sensors, controllers, and motors. Below, we provide a detailed explanation of how the 12V power is distributed to each component, accompanied by diagrams to visualize the power flow.

1. Power Source: 12V Lithium-ion Battery

The 12V Lithium-ion battery provides a stable and sufficient voltage supply for the entire system. It’s selected for its high energy density, stable output, and ability to handle high discharge rates, which are essential for driving motors and powering sensors and controllers.

2. Voltage Regulation and Distribution

Given the diverse voltage requirements of the components in our robot, we use voltage regulators and the motor driver to distribute the 12V power efficiently:

• A. Direct 12V Supply to High-Voltage Components

  • L298N Motor Driver:
    • The L298N Motor Driver directly utilizes the 12V supply to drive the motors. The motor driver is capable of handling the 12V input and converts it to the necessary output to control the DC motors.
    • Reason: The motors require higher voltage for effective operation, and the L298N motor driver is designed to manage this directly from the 12V source.

• B. Step-Down Voltage for Low-Voltage Components

  • 5V Regulated Supply for Microcontroller and Sensors:
    • Voltage Regulator: A 12V to 5V voltage regulator is used to step down the voltage for components that require a 5V input, such as the Arduino Mega 2560, HC-SR04 Ultrasonic Sensors, and the MPU-6050 IMU.
    • Components Powered:
      • Arduino Mega 2560: The Arduino Mega is the main microcontroller that controls all operations of the robot. It operates at 5V, hence the regulated 5V supply is essential.
      • HC-SR04 Ultrasonic Sensors: These sensors measure distance using ultrasonic waves and also operate at 5V.
      • MPU-6050: This sensor module, which includes an accelerometer, gyroscope, and magnetometer, operates at 3.3V, so an additional step-down regulator might be used if needed.
    • Reason: These components require lower voltage for operation, and supplying them with 12V could damage them. The regulator ensures a stable 5V output.

3. Power Flow Diagram

To visualize the power distribution, here’s a simplified flowchart showing how the 12V Lithium-ion battery's power is divided:

[12V Lithium-ion Battery]
       |
       |---> [L298N Motor Driver] ---> Motors (12V)
       |
       |---> [12V to 5V Voltage Regulator] 
                   |---> [Arduino Mega 2560] (5V)
                   |---> [HC-SR04 Ultrasonic Sensors] (5V)
                   |---> [MPU-6050] (5V)
                   |---> [Pixy2 Camera] (5V)

For a more detailed Lithium ion safety manual, you can refer to the following resources:

• MIT Lithium Battery Safety Guidance

6.2 Bill of materials (BOM)

Component	Quantity	Fu-nction/Purpose
Arduino Mega 2560	1	Main microcontroller board that controls all operations of the robot.
L298N Motor Driver	1	Controls the direction and speed of the DC motors using PWM signals.
MG995 Servo Motor	1	Provides precise angular position control for steering mechanisms.
HC-SR04 Ultrasonic Sensors	3	Measures distance to obstacles; placed at the front, left, and right sides of the robot for obstacle detection.
12V Lithium Rechargeable Battery	1	Provides the primary power source for all components.
Pixy2 Camera	1	Detects and tracks pillars (red and green) and helps in navigation by identifying road signs.
MPU-6050 IMU	1	Measures orientation and stabilizes the car’s steering for smooth and efficient turns.
HW083 Voltage Regulator	1	Steps down the 12V input to 5V and 3.3V to power various sensors and the microcontroller.
Jumper Cables	-	Used to connect various components together on the breadboard.
Wheels	4	Allows the car to move; driven by the DC motors.
DC Motors	1	Powers the wheels to drive the car forward, backward, and turn.

Suppliers

We sourced our electronic components from the following suppliers, all based in Palestine. These suppliers deliver across the West Bank:

• HIT Electronic Store
• RoboticX Store
• Labco Ramallah

7.Hurdles and Challenges

7.1. Designing Process Challenges

1. Component Selection and Layout:

• We faced challenges in selecting compatible components and placing them efficiently within the robot's compact design. Ensuring all parts fit without overcrowding was a significant issue.

2. Cable Management:

• Managing and securing the wiring was difficult. Cables frequently broke or disconnected when the robot hit obstacles, leading to unreliable connections and functionality.

3. Prototyping and Iteration:

• The design underwent multiple revisions due to various issues, such as improper component placement and structural weaknesses. Each prototype revealed new challenges, requiring adjustments and improvements.

4. Dimension and Fit Issues:

• We encountered problems with the robot's dimensions, leading to misalignment and cramped spaces for components. This necessitated redesigning the layout to ensure everything fit properly.

5. Material Selection:

• Choosing the right material for the robot's structure was difficult. Initial materials were too fragile, causing breakages. After testing several options, we settled on wood for its strength and durability.

7.2. Sensor and Coding Challenges

TCS3200 Color Sensor

• Challenge: Inconsistent Color Detection
  • Problem: The TCS3200 color sensor struggled with accurately detecting the orange color, particularly under varying lighting conditions. Despite extensive calibration efforts, the sensor's readings remained unreliable, impacting the robot's ability to differentiate between colors accurately, which was critical for task completion.
  • Approach: We conducted several tests in controlled environments, adjusting the ambient light and altering the sensor's positioning. Additionally, multiple software filters were applied to stabilize the sensor's output.
  • Result: Despite these efforts, the sensor continued to underperform in distinguishing orange from similar hues. This led us to explore alternative sensors.
  • Reference: TCS3200 Datasheet

MPU-6050 IMU

• Challenge: Gyroscope Drift and Accuracy
  • Problem: The MPU-9250, which combines a gyroscope, accelerometer, and magnetometer, exhibited significant drift over time, leading to inaccuracies in the robot's orientation and steering control. This drift made it difficult to maintain stable and precise movements, especially during prolonged operations.
  • Approach: We implemented a rigorous calibration routine to minimize offset errors and employed complementary filtering to combine data from the accelerometer and gyroscope, thereby reducing the impact of drift over time. Below is a snippet of the PID control algorithm used to correct the robot's steering based on IMU data.
  • Code Snippet:

    // Complementary filter implementation
    yaw_angle += gyro_z * elapsed_time;
    error = yaw_angle - targetYawAngle;
    integral += error * elapsed_time;
    derivative = (error - previousError) / elapsed_time;
    controlSignal = Kp * error + Ki * integral + Kd * derivative;
    myServo.write(constrain(controlSignal + 90, 40, 140));
    previousError = error;

  • Result: The adjustments led to a significant improvement in the robot's navigation stability, although some minor drift remained. This experience underscored the importance of sensor fusion techniques in robotics.
  • Reference: MPU-6050 Datasheet and specification

First-Time Experience with Arduino and Sensors

• Challenge: Steep Learning Curve with New Hardware and Software
  • Problem: This project marked our first experience with Arduino, C++, and the sensors we utilized. We faced challenges in understanding the communication protocols, particularly I2C for the IMU and PWM for the servo motor, which led to initial integration difficulties.
  • Approach: We relied heavily on online resources, including official Arduino documentation, community forums, and GitHub repositories, to troubleshoot issues and enhance our understanding of sensor integration. Practical experimentation and iterative testing were crucial in overcoming these obstacles.
  • Outcome: Through this process, we not only resolved the immediate issues but also gained substantial experience and confidence in working with embedded systems and microcontroller programming. This learning experience has been invaluable for our future projects.

7.3. Mechanical Challenges

1. Stability Issues with the Mechanism

• Challenge: Lack of Stability in the Mechanism

  • Problem: The initial design of our robot's mechanism faced significant stability issues. The instability was particularly evident in the differential and servo components, where precise movements were critical but not consistently achieved.

• Servo Motor Accuracy:

  • Problem: The servo motor we initially used was not accurate enough to maintain consistent control over the mechanism. This lack of precision led to erratic movements and contributed to the overall instability of the robot.
  • Solution: To address this, we bypassed the gears and connected the servo motor directly. However, this solution was only partially effective, as it did not fully resolve the issue of precision.

• Differential Gear Issues:

  • Problem: The differential gears were another source of instability. Initially, we used LEGO gears, but these proved to be problematic. The LEGO gears were not securely fitting together, causing them to slip frequently and fail to transmit power effectively.
  • Solution: To overcome this, we designed and 3D-printed custom couplers and gears. These custom parts were specifically designed to fit together securely and provide the necessary stability and power transmission required for the robot's differential.

• Outcome:

  • The custom 3D-printed gears and couplers significantly improved the stability and reliability of the mechanism. By designing parts that were tailored to our specific needs, we were able to eliminate the issues caused by the original LEGO gears and ensure that the robot's movements were precise and consistent.

8. Future Work

We have plans to redesign the vehicle to reduce its size, enhancing its maneuverability on tight and complex tracks. This will allow the vehicle to perform better in various challenges. Additionally, we aim to improve the mechanism to increase overall performance and efficiency, ensuring that the vehicle meets the demands of future competitions effectively.

9.Appendices

9.1. Datasheets and Specifications

Below are the datasheets for each of the key components used in the project:

Component	Datasheet Link
Arduino Mega 2560	View Datasheet
L298N Motor Driver	View Datasheet
MG995 Servo Motor	View Datasheet
HC-SR04 Ultrasonic Sensor	View Datasheet
pixy2 camera	View Datasheet
MPU-6050 IMU	View Datasheet

9.2. References

Below are the references used throughout the project documentation:

1. PID Control - A detailed guide on PID control in robotics.

2. Proportional-Derivative (PD) Controller - Explanation of the PD controller and its applications.

3. When and Why to Use P, PI, PD, and PID Controllers - An overview of different types of controllers and their uses.

4. LiPo Battery Safety Warnings - Important safety information about using LiPo batteries.

5. World Robot Olympiad - Future Engineers - Guidelines and resources for participating in the World Robot Olympiad.

6. Getting Started with the HC-SR04 Ultrasonic Sensor - A guide to using the HC-SR04 Ultrasonic Sensor with Arduino.

7. Controlling DC Motor with Arduino Motor Shield Rev3 - An Arduino tutorial on controlling a DC motor using the Motor Shield Rev3.

10. Reflections and Gratitude

Over the past three months, we've worked closely together as a team, under the dedicated supervision of our mentor, Eng. Mohammad Muamar. His unwavering support, both technically and morally, has been invaluable throughout this journey. We are deeply grateful for his guidance and the time he invested in helping us reach our goals.

This experience has been a remarkable one for each of our three team members. We ventured into new areas of technology, learning not just the technical aspects but also the importance of time management, teamwork, and the collective spirit needed to succeed.

We would also like to extend our heartfelt thanks to the organizers of this competition. Their efforts in creating such a challenging and inspiring event have provided us with an opportunity to grow, learn, and apply our skills in a real-world context.

This competition has been an unforgettable journey, teaching us lessons that extend far beyond the challenges we faced. We look forward to carrying these lessons forward in our future endeavors.