UAV Route Optimization

This repository contains a project focused on optimizing Unmanned Aerial Vehicle (UAV) routes for efficient and cost-effective navigation. The solution incorporates advanced algorithms and mathematical models to ensure optimal route planning.

Introduction

Unmanned Aerial Vehicles (UAVs) play a significant role in various industries, including logistics, surveillance, and environmental monitoring. Efficient route optimization is critical for minimizing energy consumption, reducing operational costs, and ensuring timely delivery. This project aims to address these challenges by implementing robust route optimization algorithms.

Features

• Optimal Route Planning: Ensures minimum energy usage and cost.
• Flexible Algorithm Integration: Supports various optimization techniques like Genetic Algorithms, Ant Colony and Simulated Annealing.
• Scalable Design: Can handle multiple UAVs and complex route scenarios.
• Visualization Tools: Displays optimized routes for better understanding and analysis.

Algorithm Evaluation & Comparison

After running the algorithms 100 times with the same input, the outputs are as in the table. As a result, the average genetic algorithm cost is 16% less than the average simulated annealing algorithm cost. Genetic Algorithm and Ant Colony algorithm costs are almost equal but Ant Colony beats Genetic Algorithm with 1.17% difference. Whereas Genetic Algorithm best cost slighlty better than the Ant Colony best cost. Despite that Standard Deviation of Ant Colony executions 236.36 and Standard Deviation of Genetic Algorithms is 1346.20. So Ant Colony Standard Deviation 82.2% lower than Genetic Algorithm Standard Deviation. This analysis indicates Ant Colony algorithm generates more Consistent and Stable results.

Run	SimulatedAnnealingCost	GeneticAlgorithmCost	AntColonyAlgorithmCost
1	52380.371568500275	37915.79059298257	38435.56212417904
2	50660.10815550388	38640.687078658906	37950.86388960772
3	43118.13231865587	41341.65441974222	37950.86388960771
.	.	.	.
.	.	.	.
.	.	.	.
97	47614.44035412733	37915.79059298257	38191.310622302866
98	47800.36011347326	37915.790592982565	38244.911705488856
99	46356.56919404552	37915.79059298257	38146.04891815241
100	44833.89282566424	37915.79059298257	38148.31884491421
Average	48745.11326972345	38703.14466324456	38246.853112397686
StdDev	4140.53	1346.20	263.36
Min	39970.05	37915.79	37950.86
Max	57810.64	44764.163	39269.48

Simulated Annealing Visualization

Genetic Algorithm Visualization

Ant Colony Visualization

Installation

1. Clone the repository:

   git clone https://github.com/firatyll/UAV-Route-Optimization.git

2. Navigate to the project directory:

   cd UAV-Route-Optimization

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Ensure all dependencies are installed.
2. Run the main script:

   python main.py

3. Follow the prompts to input parameters for the UAV route optimization.
4. Visualize the output routes and analyze the performance metrics.

Contributing

Contributions are welcome! If you'd like to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Make your changes and commit them:

   git commit -m "Add feature description"

4. Push to your branch:

   git push origin feature-name

5. Open a pull request.