AeroNav Sim

Autonomous drone flight simulation for experimenting with UAV navigation algorithms — path planning, flight control and multi-drone coordination — before moving to real hardware.

Python ROS PX4 / MAVROS A* Path Planning PID Control
View on GitHub
AeroNav Sim autonomous drone simulation hero AeroNav Sim path planning and control overview

Overview

AeroNav Sim is a Python-based autonomous drone simulation platform built to experiment with UAV navigation algorithms in a safe virtual environment. It integrates path planning, flight control and multi-drone coordination, with a ROS-ready architecture designed to extend toward Gazebo, MAVROS and PX4 for eventual hardware deployment.

Purpose — Why This Project Exists

Testing UAV navigation logic directly on physical hardware is expensive, slow and genuinely risky — a bad path-planning bug can mean a crashed drone. AeroNav Sim exists to de-risk that process: navigation, obstacle-avoidance and multi-drone coordination algorithms get validated in simulation first, with a ROS-ready path to real flight controllers once they're proven safe.

Animated Workflow

1
Start / Goal Setup
Drone start position and target defined.
2
A* Path Planning
Obstacle-aware route computed via graph search.
3
PID Flight Control
Smooth waypoint-following control loop.
4
Obstacle Avoidance
Real-time replanning around static/dynamic obstacles.
5
Multi-Drone Coordination
Separation monitoring across shared airspace.
6
Visualization & Recording
Matplotlib rendering plus frame-to-GIF export.

Key Features

Multi-Drone Simulation — multiple UAVs in shared airspace with independent routes and separation monitoring.
A* Path Planning — graph-search obstacle-aware route generation.
PID Flight Control — smooth waypoint following.
Obstacle Avoidance — rectangles, circles, random obstacles and inter-drone collision prevention.
ROS-Ready Architecture — integration path toward Gazebo, MAVROS and PX4.
Visual Debugging & Recording — Matplotlib rendering of trajectories, plus frame-to-GIF export.

Benefits

De-Risks Real Flight

Algorithms are proven safe in simulation before touching hardware.

Multi-Drone Testing

Validates fleet coordination without needing multiple physical drones.

ROS-Ready Path

Clear extension route to Gazebo, MAVROS and PX4 flight controllers.

Faster Iteration

Visual debugging and GIF recording speed up algorithm tuning.

Technology Stack

Python NumPy Matplotlib Pygame ROS PX4 MAVROS Gazebo
Explore the Repository