This project focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.

• Python 3.8+

git clone https://github.com/PeriniM/Multi-Agents-HAED.git
cd distributed-system-project
python -m venv env
source env/bin/activate
pip install -r requirements.txt

git clone https://github.com/PeriniM/Multi-Agents-HAED.git
cd distributed-system-project
python -m venv env
./env/Scripts/activate
pip install -r requirements.txt

• 📄main.py
• 📂Classes
  • 📄Agent.py
  • 📄EKF.py
  • 📄Environment.py
  • 📄RobotAssigner.py
  • 📄Sensors.py
  • 📄Shapes.py
  • 📄VoronoiHandler.py

• Set the voronoi points and number of agents in the main.py file
• Run the main.py file

• Ahmed et al. delve deep into the intricacies of the K-Means clustering algorithm, providing a comprehensive survey and evaluation of its performance in their paper The K-Means Algorithm.

• The challenges and solutions for the traveling salesman problem (TSP) have been extensively documented, most notably by Jünger et al. in The traveling salesman problem.

• A meticulous comparison of various algorithms geared toward solving the TSP sheds light on the complexities of the problem.

• The integration of LIDAR in robot navigation is an emerging field, exemplified by research on LIDAR-based robot navigation.

• An all-encompassing overview of various strategies for autonomous mobile robot path planning can be found in Autonomous Mobile Robot Path Planning Algorithms.

MIT