Autonomous UAV Algorithm Handbook

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Getting Started ​

Prerequisites ​

  • Python 3.12+
  • uv (recommended) or pip

Installation ​

bash
git clone https://github.com/guilyx/autonomous-quadrotor-guide.git
cd autonomous-quadrotor-guide
uv sync

Running a Simulation ​

Every simulation lives under src/uav_sim/simulations/<category>/<name>/ and can be run as a Python module:

bash
python -m uav_sim.simulations.estimation.ekf

This produces a GIF animation and a JSON log file in the simulation directory.

Building a Simulation Programmatically ​

For open-source users integrating their own algorithms, use the composition API:

python
from pathlib import Path

from uav_sim.simulations import (
    StaticPathPlanner,
    create_environment,
    create_mission,
    create_sim,
    run_sim,
    spawn_quad_platform,
)
from uav_sim.simulations.common import figure_8_path
from uav_sim.simulations.standards import SimulationStandard

standard = SimulationStandard.flight_coupled()
planner = StaticPathPlanner(
    figure_8_path(duration=standard.duration, dt=0.15, alt=12.0, alt_amp=0.0, rx=8.0, ry=6.0)
)
env = create_environment(n_buildings=0, world_size=30.0)
platform = spawn_quad_platform()
mission = create_mission(
    path=planner.plan(world=env.world, standard=standard),
    standard=standard,
    fallback_policy="none",
)
sim = create_sim(name="my_first_sim", out_dir=Path("."), mission=mission)
result = run_sim(sim=sim, env=env, platform=platform)
print(result.mission.completion.timeout_reason)

Running Tests ​

bash
uv run pytest tests/

Project Structure ​

src/uav_sim/
├── vehicles/          # 6DOF dynamics (Quadrotor, Fixed-Wing, VTOL)
├── control/           # Cascaded PID, State Machine
├── sensors/           # GPS, IMU, Lidar, Camera, Gimbal
├── estimation/        # EKF, UKF, Complementary, Particle Filter
├── perception/        # Occupancy mapping, SLAM, visual servoing
├── path_planning/     # A*, RRT*, PRM, Coverage
├── path_tracking/     # PID, LQR, Pure Pursuit
├── trajectory_planning/  # Min-Snap, Polynomial, Frenet
├── trajectory_tracking/  # Feedback Lin., MPPI, NMPC
├── swarm/             # Reynolds, Consensus, Virtual Structure
├── costmap/           # Occupancy grid, inflation layers
├── environment/       # World, obstacles, buildings
├── visualization/     # 3-panel viz, vehicle artists
└── simulations/       # 40+ runnable demos