Hardware components | ||||||
 |
| × | 1 | |||
EduBot Explorer is an educational robotics simulation platform built with PyQt5 that provides a comprehensive virtual environment for learning robotics fundamentals. This interactive application allows users to control a virtual robot through both manual operation and autonomous navigation modes within a simulated 2D environment.
Designed as a complete standalone learning experience, the simulation accurately mimics real-world robotic behavior including movement control, sensor feedback interpretation, and power management. The modular architecture ensures seamless transition from simulation to physical hardware implementation using platforms like Raspberry Pi.
🎯 Objectives- Teach core robotics concepts through interactive simulation
- Foster intuitive understanding of sensor feedback and control logic
- Encourage exploration, creativity, and problem-solving
- Provide a direct path to hardware implementation using Raspberry Pi and wheeled robots
🕹️ Control Interface
- Intuitive directional control buttons (Forward, Backward, Left, Right)
- Emergency stop functionality for immediate response
- Autonomous navigation mode with target selection
📡 Sensor Simulation
- Real-time distance measurement simulation
- Environmental temperature monitoring
- Dynamic battery level tracking with visual feedback
🗺️ Interactive Environment
- Grid-based map with coordinate system
- Obstacle placement and visualization
- Live robot position tracking with trail drawing
- Target positioning and navigation
📊 Feedback Systems
- Comprehensive command log with timestamped actions
- Visual battery indicator with color-coded alerts
- Real-time sensor data display
🧩 Advanced Capabilities
- Autonomous pathfinding to user-defined targets
- Random target generation for exploration challenges
- Environment reset and return-to-home functions
1. Library Imports
import sys, random, math
from PyQt5.QtWidgets import (...)
from PyQt5.QtCore import Qt, QTimer, QPointF
from PyQt5.QtGui import QBrush, QColor, QPen, QFontEssential libraries for GUI creation, mathematical operations, and randomizations:
- sys: System-specific parameters and functions
- random: Random number generation for sensor simulation
- math: Mathematical operations for navigation calculations
- PyQt5.QtWidgets: GUI components (buttons, labels, layouts, etc.)
- PyQt5.QtCore: Core non-GUI functionality (timers, constants)
- PyQt5.QtGui: Graphical elements (colors, brushes, pens)
2. Main Class Definition
class EduBotExplorer(QWidget):Primary class inheriting from QWidget to create the main application window:
- Creates the main application window
- Manages all UI components and robot logic
- Handles user interactions and autonomous operations
3. Initialization Method
def __init__(self):Initializes the application:
- Sets window title and geometry
- Initializes robot position and tracking variables
- Configures UI elements and timers
- Sets up the initial map environment
4. Autonomous Navigation
def autonomous_move(self):Implements autonomous navigation logic:
- Calculates direction vector to target using trigonometry
- Normalizes movement for smooth navigation (5 pixels per step)
- Checks if target is reached (within 15 pixel radius)
- Implements basic obstacle-free pathfinding
5. Boundary Checking
new_x = max(10, min(self.robot_x + dx, 380))Ensures robot stays within map boundaries:
- Prevents collision with walls (10px buffer from edges)
- Uses min/max functions to constrain movement
- Maintains robot within 400x300 pixel map area
6. UI Setup Method
def setup_ui(self):Creates and configures all user interface elements:
- Control buttons with color-coded styling
- Sensor display and battery status indicators
- Interactive map with QGraphicsView
- Autonomous driving control panel
- Command log and position tracking
7. Map Drawing Function
def draw_map(self):Creates the visual environment:
- Draws grid lines for spatial reference
- Adds border around the map area
- Places static obstacles at predetermined positions
- Uses QGraphics items for efficient rendering
8. Event Handling System
def setup_connections(self):Connects UI elements to their corresponding functions:
- Links button clicks to movement commands
- Connects autonomous mode checkbox to toggle function
- Associates target button with selection mode
- Ties autonomous timer to movement function
9. Sensor Simulation
def update_sensors(self):Simulates real-world sensor data:
- Generates random distance measurements (10-100cm)
- Creates temperature readings (20-35°C)
- Simulates battery drain over time
- Updates visual indicators based on sensor values
10. Path Tracking
def move_robot(self, dx, dy, command):Handles robot movement and path recording:
- Updates robot position with boundary checking
- Stores movement history for trail drawing
- Creates visual trail lines between positions
- Logs all movement commands for review
🎬 Visual Demonstration
🔮 Hardware Integration PossibilityThe control logic in EduBot Explorer is designed to be modular and hardware-ready. With minimal adaptation, the same movement functions and sensor handling can be connected to:
- 🛞 Wheeled robots using motor drivers (e.g., L298N)
- 🍓 Raspberry Pi GPIO for real-world actuation and sensor interfacing
- 📟 Ultrasonic sensors for live distance measurement
- 🌡️ Temperature sensors for environmental feedback
- 🧭 IMU (Inertial Measurement Unit) for orientation, tilt, and motion tracking
- ⚙️ Wheel encoders for precise position estimation and odometry
These additions allow students to transition from virtual simulation to real-world robotics with minimal code changes. The same movement logic, sensor update routines, and feedback mechanisms can be reused to control physical robots—making EduBot Explorer not just a simulation, but a launchpad for embedded systems, autonomous navigation, and sensor fusion experiments.
We’ll reconnect in a future project once I have access to a wheeled robot platform—whether purchased or 3D-printed. For now, EduBot Explorer lays the groundwork, and the journey toward real-world robotics is just beginning.
🚀 Future DevelopmentThe platform is prepared for integration with physical wheeled robot platforms, whether commercially available or 3D-printed. Future enhancements will focus on:
- Implementing more advanced pathfinding algorithms (A*, Dijkstra)
- Adding more sensor types and environmental variables
- Creating curriculum-based challenges and missions
- Developing multi-robot collaboration scenarios
- Adding data logging and analysis capabilities
EduBot Explorer establishes a solid foundation for robotics education, providing immediate engagement through simulation while preparing learners for the transition to physical robotics implementation.
Comments