100 Drone Simulation Applications

A systematic study of 100 drone simulation scenarios across 5 domains, built with Python, NumPy, and Matplotlib 3D.

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Overview

This project implements 100 carefully selected drone simulation scenarios — from basic 1v1 pursuit to large-scale swarm coordination — building a comprehensive knowledge base of drone control algorithms and multi-agent systems.

Each scenario includes a mathematical model, Python implementation, and 3D visualization.

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Tech Stack

Component	Version	Role
Python	3.10	Primary language
NumPy	1.26	Numerical computation
SciPy	1.14.1	Optimization, interpolation
Matplotlib	3.10	3D trajectory visualization
gym-pybullet-drones	2.0	PyBullet physics interface (advanced scenarios)
conda env	drones	Environment name

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Five Domains

#	Domain	Core Problems	Scenarios
1	Pursuit & Evasion	Differential games, optimal control	S001–S020
2	Logistics & Delivery	Path planning, task assignment	S021–S040
3	Environmental & SAR	Coverage, information gathering	S041–S060
4	Industrial & Agriculture	Precision control, systematic paths	S061–S080
5	Special Ops & Entertainment	Formation control, cooperative vision	S081–S100

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Quick Start

# 1. Clone the repository
git clone https://github.com/SteveT7321/100_applications_with_drones.git
cd drones

# 2. Create and activate conda environment
conda create -n drones python=3.10
conda activate drones

# 2. Install dependencies
pip install numpy==1.26.4 scipy==1.14.1 matplotlib==3.10

# 3. Run the first scenario
python src/01_pursuit_evasion/s001_basic_intercept.py

See SETUP.md for full installation instructions.

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Repository Structure

drones/
├── src/
│   ├── base/
│   │   └── drone_base.py              # Shared point-mass drone base class
│   └── pursuit/
│       └── s001_basic_intercept.py
├── scenarios/                         # Scenario cards (math model + parameters)
│   └── pursuit/
│       ├── S001_basic_intercept.md
│       └── ...
├── outputs/                           # Simulation outputs (PNG, GIF)
│   └── s001_basic_intercept/
├── domains/                           # Domain overviews with theory background
│   ├── 01_pursuit_evasion/README.md
│   └── ...
├── docs/                              # Technical reference docs
├── MATH_FOUNDATIONS.md                # Shared mathematical foundations
├── PROGRESS.md                        # Progress tracker (100 scenarios)
└── SETUP.md                           # Environment setup guide

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Progress

Domain	Done	Total
Pursuit & Evasion	20	20
Logistics & Delivery	20	20
Environmental & SAR	10	20
Industrial & Agriculture	0	20
Special Ops & Entertainment	0	20
Total	50	100

3D Upgrades: 18 true-3D variants of S002–S020 are available under src/01_pursuit_evasion/3d/ and outputs/01_pursuit_evasion/3d/.

→ Full tracker: PROGRESS.md

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References

• Shneydor, N.A. (1998). Missile Guidance and Pursuit. Horwood.
• Isaacs, R. (1965). Differential Games. Wiley.
• Panerati, J., et al. (2021). Learning to fly — a gym environment with PyBullet physics. IROS 2021.
• Mahony, R., et al. (2012). Multirotor aerial vehicles: Modeling, estimation, and control. IEEE Robotics & Automation Magazine.