Edubot Explorer 2 : Real-world Control 🚗🚗

EduBot Explorer is an advanced robotics control system that bridges the gap between simulation and real-world robotics. This project extends the basic simulation into a fully-functional system that can control physical EduBot robots through a sophisticated Python GUI interface with autonomous navigation capabilities.

https://www.hackster.io/aula-jazmati/edubot-explorer-1-interactive-robotics-simulation-835413

🤖 Real Robot Control

• Wi-Fi Socket-based Communication: TCP/IP connection between GUI and Raspberry Pi
• Bidirectional Data Exchange: Real-time command sending and sensor data reception
• Motor Control: Precise movement commands (forward, backward, left, right, stop)
• Live Sensor Monitoring: Distance, temperature, and battery level tracking

🧭 Autonomous Navigation System

• Interactive Target Selection: Click on map to set destination
• Path Planning: Automated route calculation to target
• Obstacle Avoidance: Built-in obstacle detection and navigation
• Real-time Position Tracking: Live robot position updates

🎮 Advanced User Interface

• Compact Design: Optimized layout for better usability
• Connection Management: Easy robot connection setup
• Sensor Dashboard: Real-time environmental data display
• Command Logging: Complete history of robot operations

System Architecture Overview

System Architecture Overview

Raspberry Pi Setup

• Raspberry Pi (3/4/Zero)
• Motor Driver (L298N or similar)
• 2x DC Motors with wheels
• HC-SR04 Ultrasonic Distance Sensor
• 5V Power Supply
• Jumper Wires and Breadboard

1 / 2 • EduBot

EduBot

EduBot & GUI

Wiring Configuration

# Motor Connections
MOTOR_LEFT_FORWARD = 17
MOTOR_LEFT_BACKWARD = 18
MOTOR_RIGHT_FORWARD = 22
MOTOR_RIGHT_BACKWARD = 23
# Sensor Connections
TRIGGER_PIN = 24
ECHO_PIN = 25
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   RASPBERRY PI  │    │   L298N DRIVER  │    │     MOTORS      │
│                 │    │                 │    │                 │
│ GPIO17 ────────►│ IN1│                 │OUT1│┌─────────────┐  │
│ GPIO18 ────────►│ IN2│                 │OUT2││  LEFT MOTOR │  │
│ GPIO22 ────────►│ IN3│                 │OUT3│└─────────────┘  │
│ GPIO23 ────────►│ IN4│                 │OUT4│┌─────────────┐  │
│ 5V     ────────►│ VCC│                 │    ││ RIGHT MOTOR │  │
│ GND    ────────►│ GND│                 │    │└─────────────┘  │
└─────────────────┘    └─────────────────┘    └─────────────────┘

1 / 3 • The first step in designing FlamingoTown 🦩😄

The first step in designing FlamingoTown 🦩😄

Data Flow Analysis

Data Flow Analysis

System Components

1. Robot Server (Raspberry Pi)

class RealEduBot:
"""Hardware abstraction layer for physical robot"""
- Motor control functions
- Sensor data collection
- GPIO management
class RobotServer:
"""TCP server for handling multiple clients"""
- Socket communication
- Command processing
- Automatic sensor broadcasting

Robot Server Test

Robot Server Test

2. GUI Client (Desktop)

class RobotConnection:
"""Network communication handler"""
- Connection management
- Command sending
- Data reception
class EduBotExplorer:
"""Main application interface"""
- Interactive map
- Autonomous navigation
- Real-time monitoring

GUI Test

GUI Test

Installation & Setup

Step 1: Raspberry Pi Setup

# Install required packages
sudo apt update
sudo apt install python3-pip
pip3 install RPi.GPIO
# Clone the project
https://github.com/aula9/EduBot-Explorer2
# Run the robot server
python3 robot_server.py

Step 2: Desktop Application

# Install dependencies
pip install PyQt5
# Run the GUI application
python edubot_gui.py

Step 3: Network Configuration

1. Find Raspberry Pi IP address:

hostname -I

2. Update GUI connection settings with correct IP

3. Ensure both devices are on same network

Usage Instructions

Basic Operation

1. Connect to Robot: Enter Raspberry Pi IP and click "Connect"

2. Manual Control: Use arrow buttons for direct control

3. Set Target: Click "Set Target" and click on map for destination

4. Autonomous Mode: Enable auto mode for automated navigation

Direct Control Test

Advanced Features

• Emergency Stop: Immediate halt of all operations
• Path Clearing: Remove previous trails and targets
• Home Return: Return robot to starting position
• Sensor Monitoring: Live distance and temperature readings

Technical Implementation

Communication Protocol

{
"type": "command",
"command": "move",
"data": {
"direction": "forward",
"distance": 8.0
}
}

Autonomous Navigation Algorithm

def autonomous_move(self):
dx = self.target_x - (self.robot_x + 7)
dy = self.target_y - (self.robot_y + 7)
distance = math.sqrt(dx*dx + dy*dy)
if distance < 10:  # Target reached
return
# Normalize and apply movement
dx = dx / distance * 4
dy = dy / distance * 4
self.move_robot(dx, dy, "autonomous")

Applications & Learning Outcomes

Educational Applications

• Robotics Programming: Hands-on experience with real hardware
• Network Communication: TCP/IP socket programming
• Sensor Integration: Working with ultrasonic and environmental sensors
• Autonomous Systems: Path planning and navigation algorithms

Technical Skills Developed

• Python programming with PyQt5
• Raspberry Pi GPIO control
• Client-server architecture
• Real-time data processing
• GUI application development

Troubleshooting

Common Issues

1. Connection Failed: Check IP address and network connectivity

2. GPIO Errors: Verify wiring and pin assignments

3. Motor Issues: Check power supply and motor connections

4. Sensor Inaccuracies: Ensure proper sensor placement and calibration

Debugging Tips

• Enable verbose logging in both server and client
• Check Raspberry Pi system resources
• Verify all dependencies are installed
• Test individual components separately

- Precise Localization: Researching Rotary Encoders + IMU integration for better movement tracking

- Advanced Navigation: Developing smarter obstacle avoidance algorithms

- Enhanced Simulation: Improving position accuracy and environmental awareness

Current Status: Prototyping and testing phase - following a practical, step-by-step implementation approach.

Progress updates shared regularly as features are validated and stabilized.

Built with ❤️ for the robotics education community