Robotics with Webots
This guide will walk you through integrating Webots robotics simulator with SnapThink to create powerful robotics development workflows.
Prerequisites
Install Webots
- Download Webots from cyberbotics.com
- Follow installation instructions for your platform:
- macOS: Mount the DMG and drag to Applications
- Windows: Run the installer as administrator
- Linux: Extract and add to PATH
Verify Installation
# Check Webots is accessible from command line
webots --version
# Should output something like: Webots R2023b
Quick Start: Your First Robot Simulation
Step 1: Create a New Notebook
- Open SnapThink
- Click “New Document” → “Python Notebook”
- Name it “My First Robot Simulation”
Step 2: Set Up Webots Connection
# Initialize Webots connection
import sys
sys.path.append('/Applications/Webots.app/lib/controller/python') # macOS
# sys.path.append('C:/Program Files/Webots/lib/controller/python') # Windows
# sys.path.append('/usr/local/webots/lib/controller/python') # Linux
from controller import Robot, Motor, DistanceSensor
# Create robot instance
robot = Robot()
# Get simulation timestep
timestep = int(robot.getBasicTimeStep())
print("✅ Connected to Webots simulation!")
print(f"Simulation timestep: {timestep}ms")
Step 3: Load a Robot World
# For this example, we'll use the built-in e-puck robot
# In Webots: File → Open World → projects/robots/gctronic/e-puck/worlds/e-puck.wbt
# Initialize robot components
left_motor = robot.getDevice('left wheel motor')
right_motor = robot.getDevice('right wheel motor')
# Set motors to velocity control
left_motor.setPosition(float('inf'))
right_motor.setPosition(float('inf'))
left_motor.setVelocity(0.0)
right_motor.setVelocity(0.0)
# Initialize distance sensors
distance_sensors = []
sensor_names = ['ps0', 'ps1', 'ps2', 'ps3', 'ps4', 'ps5', 'ps6', 'ps7']
for name in sensor_names:
sensor = robot.getDevice(name)
sensor.enable(timestep)
distance_sensors.append(sensor)
print("🤖 Robot initialized with motors and sensors")
Step 4: Implement Basic Behavior
# Simple obstacle avoidance behavior
def run_obstacle_avoidance():
step_count = 0
max_steps = 1000 # Run for limited time
# Record data for analysis
sensor_history = []
position_history = []
while robot.step(timestep) != -1 and step_count < max_steps:
# Read sensor values
sensor_values = [sensor.getValue() for sensor in distance_sensors]
sensor_history.append(sensor_values.copy())
# Simple obstacle avoidance logic
left_speed = 6.28 # Base speed
right_speed = 6.28
# If obstacles detected in front, turn
if sensor_values[0] > 80 or sensor_values[7] > 80: # Front-left or front-right
# Turn right
left_speed = 6.28
right_speed = -6.28
elif sensor_values[1] > 80 or sensor_values[2] > 80: # Left side
# Turn right
left_speed = 6.28
right_speed = 3.14
elif sensor_values[5] > 80 or sensor_values[6] > 80: # Right side
# Turn left
left_speed = 3.14
right_speed = 6.28
# Apply motor speeds
left_motor.setVelocity(left_speed)
right_motor.setVelocity(right_speed)
step_count += 1
# Record position (simplified)
position_history.append({
'step': step_count,
'time': step_count * timestep / 1000.0,
'sensors': sensor_values,
'motors': [left_speed, right_speed]
})
print(f"✅ Simulation completed - {step_count} steps")
return sensor_history, position_history
# Run the simulation
print("🚀 Starting obstacle avoidance simulation...")
sensor_data, position_data = run_obstacle_avoidance()
Step 5: Analyze Results with AI
# Analyze the robot's behavior
import matplotlib.pyplot as plt
import numpy as np
# Extract data for plotting
times = [p['time'] for p in position_data]
front_sensors = [p['sensors'][0] + p['sensors'][7] for p in position_data] # Combined front sensors
motor_diff = [abs(p['motors'][0] - p['motors'][1]) for p in position_data] # Turn intensity
# Create visualization
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
# Plot sensor readings over time
ax1.plot(times, front_sensors, 'b-', linewidth=2, label='Front Obstacle Detection')
ax1.set_xlabel('Time (seconds)')
ax1.set_ylabel('Sensor Value')
ax1.set_title('Robot Obstacle Detection Over Time')
ax1.grid(True)
ax1.legend()
# Plot turning behavior
ax2.plot(times, motor_diff, 'r-', linewidth=2, label='Turning Intensity')
ax2.set_xlabel('Time (seconds)')
ax2.set_ylabel('Motor Speed Difference')
ax2.set_title('Robot Turning Behavior')
ax2.grid(True)
ax2.legend()
plt.tight_layout()
plt.show()
# Calculate performance metrics
avg_obstacle_proximity = np.mean(front_sensors)
total_turning = np.sum(motor_diff)
simulation_time = times[-1]
print(f"📊 Simulation Analysis:")
print(f" • Total simulation time: {simulation_time:.2f} seconds")
print(f" • Average obstacle proximity: {avg_obstacle_proximity:.1f}")
print(f" • Total turning activity: {total_turning:.1f}")
print(f" • Obstacles encountered: {len([x for x in front_sensors if x > 80])}")
Now ask the AI to analyze your results:
“Can you analyze this robot’s obstacle avoidance performance? What improvements would you suggest for the algorithm?”
Advanced Integration Examples
1. Multi-Robot Coordination
# Example: Two robots working together
class MultiRobotController:
def __init__(self):
self.robots = []
self.robot_positions = []
def add_robot(self, robot_name):
# In Webots, load a world with multiple robots
robot = Robot()
self.robots.append(robot)
print(f"Added robot: {robot_name}")
def coordinate_movement(self):
# Implement formation keeping or task distribution
for i, robot in enumerate(self.robots):
# Assign different behaviors to each robot
if i == 0:
self.leader_behavior(robot)
else:
self.follower_behavior(robot, i)
def leader_behavior(self, robot):
# Leader explores the environment
pass
def follower_behavior(self, robot, robot_id):
# Followers maintain formation with leader
pass
# Ask AI to help implement coordination algorithms
multi_robot = MultiRobotController()
2. Computer Vision Integration
# Robot with camera sensor
def setup_camera_robot():
robot = Robot()
camera = robot.getDevice('camera')
camera.enable(timestep)
return robot, camera
def process_camera_data(camera):
# Get camera image
image = camera.getImage()
width = camera.getWidth()
height = camera.getHeight()
# Convert to numpy array for processing
import numpy as np
# Webots image is in BGRA format
image_array = np.frombuffer(image, np.uint8).reshape((height, width, 4))
# Convert BGRA to RGB
rgb_image = image_array[:, :, [2, 1, 0]] # BGR to RGB
return rgb_image
# Use this with AI for object detection:
# "Can you help me detect red objects in this camera image?"
3. Machine Learning Integration
# Collect training data from simulation
class RobotDataCollector:
def __init__(self):
self.training_data = []
def collect_sensor_data(self, robot, action_taken):
# Get current sensor state
sensor_values = [sensor.getValue() for sensor in distance_sensors]
# Record state-action pair for learning
data_point = {
'sensors': sensor_values,
'action': action_taken,
'timestamp': robot.getTime()
}
self.training_data.append(data_point)
def export_training_data(self):
import pandas as pd
df = pd.DataFrame(self.training_data)
df.to_csv('robot_training_data.csv', index=False)
print(f"Exported {len(self.training_data)} training samples")
# Use with AI for behavior learning:
# "Can you train a neural network on this robot behavior data?"
Real-World Project Workflows
Project 1: Autonomous Warehouse Robot
# Step 1: Design the warehouse environment in Webots
# - Create shelving units, boxes, charging stations
# - Add floor markers for navigation
# Step 2: Implement robot controller
class WarehouseRobot:
def __init__(self):
self.robot = Robot()
self.state = "idle" # idle, navigating, picking, returning
self.inventory = []
self.target_location = None
def navigate_to_shelf(self, shelf_id):
# Use Webots GPS and compass for navigation
gps = self.robot.getDevice('gps')
compass = self.robot.getDevice('compass')
# Implementation details...
def pick_item(self, item_id):
# Control gripper to pick items
gripper = self.robot.getDevice('gripper')
# Implementation details...
# Step 3: Ask AI for optimization
# "How can I optimize the robot's path planning for multiple pickups?"
Project 2: Search and Rescue Robot
# Simulate disaster scenarios
class SearchRescueRobot:
def __init__(self):
self.robot = Robot()
self.victims_found = []
self.explored_areas = set()
def explore_unknown_terrain(self):
# Use SLAM algorithms for mapping
# Ask AI: "Implement a frontier-based exploration algorithm"
pass
def detect_victims(self):
# Use camera and thermal sensors
# Ask AI: "Help me detect human heat signatures in thermal images"
pass
def plan_rescue_path(self):
# Optimize path to reach all victims
# Ask AI: "Find the shortest path visiting all victim locations"
pass
# Test in various disaster scenarios
rescue_robot = SearchRescueRobot()
Project 3: Agricultural Monitoring Drone
# Simulate crop monitoring with aerial robots
class AgricultureDrone:
def __init__(self):
self.robot = Robot() # Drone in Webots
self.crop_health_data = []
self.flight_path = []
def plan_survey_flight(self, field_boundaries):
# Generate optimal flight path for coverage
# Ask AI: "Create a lawn mower pattern for field coverage"
pass
def analyze_crop_health(self, camera_image):
# Use computer vision for health assessment
# Ask AI: "Detect diseased plants in this aerial image"
pass
def generate_health_map(self):
# Create spatial map of crop conditions
import matplotlib.pyplot as plt
# Visualization code...
# Integrate with weather data and growth models
ag_drone = AgricultureDrone()
Advanced Features
Custom Robot Models
# Load custom robot from URDF or Webots proto files
def load_custom_robot(robot_file_path):
# In Webots: File → Import → Robot
# Then connect via Python API
robot = Robot()
# Auto-discover available devices
devices = []
device_count = robot.getNumberOfDevices()
for i in range(device_count):
device = robot.getDeviceByIndex(i)
device_name = device.getName()
device_type = device.getNodeType()
devices.append({'name': device_name, 'type': device_type})
print(f"Found {len(devices)} devices on robot:")
for device in devices:
print(f" • {device['name']} ({device['type']})")
return robot, devices
# Ask AI to help configure your custom robot:
# "Help me set up controllers for my 6-DOF robotic arm"
Physics Parameter Tuning
# Optimize simulation realism
def tune_physics_parameters():
# Access world physics settings through Webots API
# Adjust gravity, friction, damping, etc.
physics_configs = [
{'gravity': -9.81, 'friction': 0.8, 'bounce': 0.2},
{'gravity': -9.81, 'friction': 1.0, 'bounce': 0.1},
{'gravity': -9.81, 'friction': 0.6, 'bounce': 0.3},
]
results = []
for config in physics_configs:
# Run simulation with different physics
result = run_robot_test(config)
results.append(result)
# Ask AI to analyze which configuration is most realistic
return results
# "Which physics parameters give the most realistic robot behavior?"
Performance Monitoring
# Monitor simulation performance and robot metrics
class PerformanceMonitor:
def __init__(self):
self.metrics = {
'simulation_fps': [],
'robot_speed': [],
'energy_consumption': [],
'task_completion_time': [],
'collision_count': 0
}
def update_metrics(self, robot):
# Collect real-time performance data
current_speed = self.calculate_robot_speed(robot)
self.metrics['robot_speed'].append(current_speed)
# Monitor energy usage
motor_power = self.calculate_motor_power(robot)
self.metrics['energy_consumption'].append(motor_power)
def generate_performance_report(self):
# Create comprehensive performance analysis
import pandas as pd
report = pd.DataFrame(self.metrics)
# Ask AI to analyze performance trends
return report
monitor = PerformanceMonitor()
Troubleshooting Guide
Common Issues and Solutions
Connection Problems
# Test Webots connection
def test_webots_connection():
try:
robot = Robot()
timestep = robot.getBasicTimeStep()
print(f"✅ Webots connected - timestep: {timestep}ms")
return True
except Exception as e:
print(f"❌ Connection failed: {e}")
print("Solutions:")
print("1. Ensure Webots is running")
print("2. Check Python path includes Webots controller library")
print("3. Verify world file has robot controller set to '<extern>'")
return False
test_webots_connection()
Performance Issues
# Optimize simulation speed
def optimize_simulation():
# Reduce rendering quality for faster simulation
# In Webots: View → Rendering → Fast
# Adjust physics timestep
# Larger timestep = faster simulation, less accuracy
# Smaller timestep = slower simulation, more accuracy
recommended_settings = {
'timestep': 32, # milliseconds
'rendering': 'fast',
'run_mode': 'run', # not real-time
'physics_iterations': 1
}
print("Recommended settings for performance:")
for key, value in recommended_settings.items():
print(f" • {key}: {value}")
optimize_simulation()
Memory Usage
# Monitor and optimize memory usage
import psutil
import os
def monitor_memory():
process = psutil.Process(os.getpid())
memory_mb = process.memory_info().rss / 1024 / 1024
print(f"Current memory usage: {memory_mb:.1f} MB")
if memory_mb > 1000: # More than 1GB
print("⚠️ High memory usage detected")
print("Solutions:")
print("1. Reduce data collection frequency")
print("2. Clear old simulation data")
print("3. Restart simulation periodically")
return memory_mb
# Run periodically during long simulations
current_memory = monitor_memory()
Best Practices
Code Organization
# Structure your robotics projects
"""
robotics_project/
├── robots/ # Robot controller code
├── worlds/ # Webots world files
├── data/ # Simulation results
├── analysis/ # Data analysis notebooks
└── docs/ # Project documentation
"""
# Use classes for robot controllers
class BaseRobotController:
def __init__(self, robot_name):
self.robot = Robot()
self.name = robot_name
self.initialize_devices()
def initialize_devices(self):
# Override in subclasses
pass
def run_behavior(self):
# Override in subclasses
pass
class NavigationRobot(BaseRobotController):
def initialize_devices(self):
# Specific device setup for navigation
pass
Experimental Design
# Design reproducible experiments
class RoboticsExperiment:
def __init__(self, experiment_name):
self.name = experiment_name
self.parameters = {}
self.results = []
self.random_seed = 12345
def set_parameters(self, **params):
self.parameters.update(params)
def run_trial(self, trial_number):
# Set random seed for reproducibility
import random
random.seed(self.random_seed + trial_number)
# Run simulation trial
result = self.execute_simulation()
self.results.append(result)
return result
def analyze_results(self):
# Statistical analysis of results
# Ask AI: "Analyze these experimental results for significance"
pass
# Example usage
exp = RoboticsExperiment("Obstacle Avoidance Performance")
exp.set_parameters(robot_speed=2.0, sensor_range=0.5)
Documentation and Sharing
# Always document your robotics projects:
## Project: Autonomous Navigation Robot
**Objective**: Develop obstacle avoidance for mobile robot
**Environment**: Office-like setting with furniture obstacles
**Robot**: Differential drive robot with 8 distance sensors
**Algorithm**: Reactive obstacle avoidance with wall following
### Results Summary
- Successfully navigated 95% of test scenarios
- Average completion time: 45 seconds
- Energy efficiency: 15% better than baseline
### Key Insights from AI Analysis
- Robot tends to favor right turns (bias in algorithm)
- Performance degrades in narrow corridors
- Sensor fusion could improve obstacle detection
### Future Improvements
- Implement path planning for global navigation
- Add camera for better obstacle classification
- Test in dynamic environments with moving obstacles
Resources and Next Steps
Webots Learning Resources
Integration with SnapThink Features
- Document Analysis - Analyze robotics research papers
- Python Environment - Advanced data analysis
- AI Chat - Get help with robotics algorithms
Community and Support
- Join the Webots Discord community
- Share your projects in the SnapThink Community
- Contribute to robotics examples and tutorials
Ready to build amazing robots? Start with the quick start example above and ask the AI for help along the way!