Ant Simulation

This repository contains an advanced Ant Colony Simulation that demonstrates emergent behavior through pheromone-based pathfinding and collective food gathering.

Authors and Contributions

Chia Da Liu

• Obstacle implementation and collision detection
• Ant steering behavior and obstacle avoidance
• Core ant movement mechanics
• Simulation physics and navigation

Yu Chun Lin

• Food system implementation
• Pheromone tracking mechanism
• Nest functionality and return behavior
• Food counting system
• Right-click food spot creation

Ting Chia Chen

• Soldier ant implementation and behavior
• Teamwork interaction between worker and soldier ants
• Enhanced soldier ant protection logic
• Game mechanics adjustments for ant behaviors

Features

Core Mechanics

• Realistic ant movement with steering behaviors
• Obstacle avoidance system
• Pheromone-based communication
• Food gathering and transportation
• Nest-centered colony behavior

Ant Types

1. Worker Ants:

   • Responsible for foraging food and gathering resources.
   • Exhibit pheromone-following behaviors to lead others to food sources.
   • Carry food back to the nest and deposit it for storage.

2. Soldier Ants:

   • Designed to protect the colony and worker ants.
   • Move towards nearby worker ants when they are in danger.
   • Maintain a distance from the nest while ensuring the safety of worker ants during food collection.

New Obstacle Types

1. Heart Shape Obstacle

• A heart-shaped obstacle that creates interesting patterns in the simulation.
• This shape adds a unique visual element and tests the ants' navigation skills.

2. Circular Obstacle

• A circular obstacle that provides a smooth barrier for the ants.
• This shape encourages ants to navigate around it rather than directly through it.

3. Square Obstacle

• A square-shaped obstacle that represents typical barriers.
• It introduces a standard challenge for ant movement and pathfinding.

4. Randomly Placed Obstacles

• Obstacles that are placed at random locations within the simulation area.
• This adds unpredictability to the environment, requiring ants to adapt their strategies.

Advanced Behaviors

1. Food Collection System:

   • Ants search for food sources
   • Food is gathered in concentrated spots
   • Automatic food replenishment
   • Visual feedback when ants carry food

2. Pheromone System:

   • Dynamic pheromone trails
   • Strength-based pheromone following
   • Pheromone decay over time
   • Different pheromone types (food and home trails)

3. Anti-Clustering Mechanisms:

   • Memory-based movement patterns
   • Stuck detection and resolution
   • Directional persistence
   • Random exploration factors

4. **Teamwork Behavior: • Soldier ants move to protect worker ants when they are nearby. • Worker ants benefit from soldier protection, ensuring safer food collection.

Pheromone Coefficients and Behavior Parameters

Pheromone Strengths

PHEROMONE_STRENGTHS = {
    'carrying_food': 300,    # Strong trail when carrying food
    'returning_to_food': 200,  # Medium-strong trail when returning to known food
    'experienced': 100,      # Medium trail for experienced ants
    'exploring': 20         # Weak trail during exploration
}

Movement Parameters

MOVEMENT_WEIGHTS = {
    'wander_force': 0.3,    # Base random movement
    'avoid_force': 2.0,     # Obstacle avoidance priority
    'pheromone_influence': {
        'carrying_food': 2.0,
        'returning': 1.5,
        'exploring': 0.4
    }
}

Anti-Clustering Parameters

ANTI_CLUSTERING = {
    'memory_length': 30,    # Positions to remember
    'stuck_threshold': 40,  # Distance threshold for stuck detection
    'direction_persistence': {
        'min': 30,
        'max': 60
    }
}

Pheromone Demo Video

Add Foods Demo Video

Obstacle Shapes

The simulation includes four types of obstacles, each represented in the following GIFs:

1. Heart-shaped Obstacle:
   • A unique heart shape that presents a challenge for the ants.

2. Circular Obstacle:
   • A circular obstacle that requires strategic navigation.

3. Square Obstacle:
   • A square obstacle that blocks paths and affects ant movement.

4. Randomly Positioned Obstacle:

   • Obstacles randomly placed throughout the environment.

   

Soldier Ant

The soldier ant is a specialized type of ant responsible for protecting worker ants during food collection. Below are the characteristics of the soldier ant:

• Protection of Worker Ants: Soldier ants actively approach nearby worker ants to provide protection, ensuring their safety during food gathering.

• Behavior Demonstration: Soldier ants patrol around the nest to offer support when needed.

  

Installation

Cloning the Repository

git clone https://github.com/Yoppman/Ant-Simulation.git
cd Ant-Simulation

Installing Dependencies

Make sure you have Python installed. Install required libraries:

pip install -r requirements.txt

Requirements

• Python 3.x
• Pygame 2.x

Running the Program

Launch the simulation:

python main.py

Controls and Interaction

• Right Click: Add new food source
• Close Window: Exit simulation
• Food counter displays in top-left corner

Implementation Details

Ant States

1. Exploring:

   • Higher random movement
   • Lower pheromone influence
   • Active obstacle avoidance

2. Food Found:

   • Strong pheromone trail creation
   • Direct path to nest
   • Minimal random movement

3. Returning to Food:

   • Medium pheromone following
   • Remembered position influence
   • Moderate random movement

Pheromone System Details

1. Trail Creation:

   • Stronger trails when carrying food
   • Medium strength for return trips
   • Weak trails during exploration
   • Automatic decay over time

2. Following Behavior:

   • Weighted influence based on pheromone strength
   • Distance-based effectiveness
   • Random variation to prevent clustering
   • Experience-based following efficiency

3. Anti-Clustering Mechanics:

   • Position memory system
   • Stuck detection algorithms
   • Direction persistence
   • Random force injection

Future Enhancements

• Different ant types (workers, soldiers)
• Multiple food types
• Dynamic obstacle generation
• Weather effects on pheromones
• Colony growth mechanics
• Predator-prey interactions

Contributing

Feel free to fork the repository and submit pull requests. Areas for improvement include:

• Performance optimization
• New ant behaviors
• UI improvements
• Additional environment features

License

This project is open source and available under the MIT License.