Robot-Charging-in-Basement-Grid-map-Using-GAMA

This project involves a simulation using GAMA to model and analyze robot charging strategies within a basement grid map environment. The primary goal is to optimize the movement and charging behavior of robots to ensure efficient energy usage and task completion.

Features

• Simulation Environment: The simulation is set up in a basement grid map environment.
• Algorithms: Multiple pathfinding algorithms are implemented, including A*, Dijkstra, JPS, BF, and a custom algorithm named Thinh.
• Robot Behavior: Robots are programmed to navigate the grid, avoiding obstacles and seeking charging stations when necessary.
• Energy Management: The simulation monitors robot energy levels and initiates charging when energy falls below a certain threshold.
• Statistics Collection: The simulation collects data on path lengths and charging events to analyze performance.

Parameters

• Scenario: Choose between "random map" and "basement map".
• Algorithm: Select from A*, Dijkstra, JPS, BF, and Thinh.
• Neighborhood Type: Specify the type of neighborhood (4 or 8).
• Obstacle Rate: Set the rate of obstacles in the grid (0.0 to 0.4).
• Grid Size: Define the height and width of the grid (default 100x100).
• Energy Limit: Set the maximum energy limit for robots (0 to 2500).

Setup

1. Clone the Repository:

   git clone https://github.com/Thinhvip9999/Robot-Charging-in-Basement-Grid-map-Using-GAMA.git
   cd Robot-Charging-in-Basement-Grid-map-Using-GAMA

2. Open the Project in GAMA:

   • Open GAMA and load the project files.

3. Run the Simulation:

   • Configure the parameters as needed.
   • Start the simulation to see the robots navigate and charge within the grid.

Files

• README.md: Project documentation.
• Algorithms_On_Map.gaml: The main simulation model file.
• includes/images/robot.png: Image file for robot representation.
• includes/images/charger.png: Image file for charging station representation.
• includes/images/electric-car.png: Additional image file used in the simulation.

Contributions

Contributions are welcome! Please fork the repository and submit pull requests for any improvements or new features.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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