Kasper Rømer Grøntved

Phd-Candidate @ UAS Center, MMMI, University of Southern Denmark

3 minute read

Introduction to Coverage Path Planning

Welcome to the world of autonomous coverage planning! If you’re new to robotics or UAV operations, you might wonder: “How do robots efficiently cover large areas without missing spots or wasting time?” This is where TrajGenPy comes in - a powerful Python library that solves this exact problem using the Boustrophedon Cell Decomposition algorithm.

What is Coverage Path Planning?

Imagine you need to:

  • Survey an agricultural field with a drone
  • Clean a room with a robotic vacuum
  • Search a wilderness area for missing persons
  • Inspect a solar farm for maintenance

In all these cases, you want to cover every inch of the area efficiently. Coverage path planning algorithms help generate optimal routes that ensure complete coverage while minimizing travel time and energy consumption.

Why TrajGenPy?

TrajGenPy stands out because it:

  • Integrates with real-world data: Extract features directly from OpenStreetMap
  • Handles complex environments: Works with obstacles, boundaries, and irregular shapes
  • Provides geodetic accuracy: Converts between coordinate systems seamlessly
  • Offers high performance: Python bindings for optimized C++ algorithms

Setting Up Your Environment

Prerequisites

Before diving in, make sure you have these dependencies installed:

# Ubuntu/Debian systems
sudo apt-get update && apt-get -y install libcgal-dev pybind11-dev

# Install TrajGenPy
pip install trajgenpy

Your First Coverage Path

Let’s start with a simple example that demonstrates the core concepts:

import shapely
from matplotlib import pyplot as plt
from trajgenpy import Geometries

# Step 1: Define the area you want to cover
# This could be a field, building, or any polygonal area
coverage_area = shapely.Polygon([
    [0, 0], [100, 0], [100, 50], [0, 50]  # Simple rectangle
])

# Step 2: Create a GeoPolygon and set coordinate system
geo_area = Geometries.GeoPolygon(coverage_area)
geo_area.set_crs("EPSG:3857")  # Web Mercator projection

# Step 3: Generate the coverage pattern
# The offset determines how close your paths are to each other
offset = Geometries.get_sweep_offset(
    spacing=5,        # 5-meter spacing between paths
    angle=0,          # 0 degrees (horizontal paths)
    altitude=30       # 30-meter flight altitude
)

# Step 4: Create the sweep pattern
polygon_list = Geometries.decompose_polygon(geo_area.get_geometry())
coverage_paths = []

for polygon in polygon_list:
    sweeps = Geometries.generate_sweep_pattern(
        polygon, 
        offset, 
        clockwise=True,      # Direction of traversal
        connect_sweeps=True  # Connect path segments
    )
    coverage_paths.extend(sweeps)

# Step 5: Visualize your results
geo_area.plot(facecolor="lightblue", alpha=0.5, edgecolor="blue")
multi_traj = Geometries.GeoMultiTrajectory(coverage_paths, "EPSG:3857")
multi_traj.plot(color="red", linewidth=2)
plt.title("Coverage Path for Simple Rectangle")
plt.axis("equal")
plt.show()

Understanding the Results

When you run this code, you’ll see:

  • Blue area: The region you want to cover
  • Red lines: The optimal path your robot/drone should follow

The algorithm automatically:

  1. Decomposes complex shapes into simpler ones
  2. Generates parallel sweep lines
  3. Connects path segments efficiently
  4. Avoids unnecessary backtracking

Handling Real-World Complexity

Real environments aren’t simple rectangles. Let’s add obstacles:

# Define an obstacle (building, tree, restricted area)
obstacle = shapely.Polygon([
    [30, 15], [70, 15], [70, 35], [30, 35]
])

obstacle_geo = Geometries.GeoPolygon(obstacle)
obstacle_geo.set_crs("EPSG:3857")

# Generate coverage considering obstacles
polygon_list = Geometries.decompose_polygon(
    geo_area.get_geometry(), 
    obstacles=obstacle_geo.get_geometry()  # Add obstacles here
)

# The rest of the code remains the same...

Key Parameters Explained

Understanding these parameters helps you optimize for your specific use case:

Spacing

  • Smaller values (1-2m): Higher resolution, longer mission time
  • Larger values (10-20m): Faster coverage, lower resolution
  • Rule of thumb: Use 2-3x your sensor footprint width

Angle

  • 0°: Horizontal paths (good for east-west winds)
  • 90°: Vertical paths (good for north-south winds)
  • 45°: Diagonal paths (can be more efficient for square areas)

Altitude

  • Lower: Better sensor resolution, shorter range
  • Higher: Larger coverage area per pass, safety buffer

Best Practices for New Users

  1. Start Simple: Begin with rectangular areas before complex shapes
  2. Test Parameters: Experiment with spacing and angles for your specific application
  3. Consider Constraints: Factor in battery life, weather, and regulations
  4. Validate Results: Always visualize paths before deployment
  5. Real-World Testing: Start with small areas for initial field tests

Next Steps

Now that you understand the basics of TrajGenPy:

  1. Try the examples with your own coordinates
  2. Experiment with different shapes and obstacles
  3. Learn about coordinate systems (we’ll cover this in detail next)
  4. Explore OpenStreetMap integration for real-world scenarios

In our next post, we’ll dive into multi-robot task allocation using TrajAllocPy, where multiple agents work together to cover large areas efficiently.

Common Troubleshooting

Issue: “Module not found” errors Solution: Ensure all dependencies are installed and you’re using Python 3.10+

Issue: Coordinate system errors Solution: Always specify a valid CRS (Coordinate Reference System) like “EPSG:3857”

Issue: Empty or strange paths Solution: Check that your polygon is valid and doesn’t self-intersect

Ready to plan your first coverage mission? Try the examples above and share your results! In the next guide, we’ll explore how to coordinate multiple robots for even more efficient coverage.

Updated:

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