AI-MultiBotControl

A sophisticated multi-robot control system that implements market-based task allocation and intelligent path planning in a dynamic environment. The system features a graphical interface built with Pygame, demonstrating autonomous robot coordination and task management.

Features

• Dynamic Task Allocation: Market-based auction system for optimal task distribution among robots
• Intelligent Path Planning: A* algorithm implementation for efficient navigation
• Collision Avoidance: Smart collision detection and resolution between robots
• Dynamic Environment:
  • Random task generation during simulation
  • Moving obstacles that robots must avoid
  • Priority-based task system (P1, P2, P3)
• Performance Metrics: Comprehensive metrics tracking system for evaluating efficiency
• Reinforcement Learning Integration: DQN-based learning for optimizing task allocation
• Interactive GUI: User-friendly interface for:
  • Manual robot placement
  • Obstacle positioning
  • Task creation
  • Random environment generation
  • Real-time simulation control

Project Structure

src/
├── agents/             # AI and pathfinding algorithms
│   ├── astar.py       # A* pathfinding implementation
│   └── RL/            # Reinforcement learning components
├── core/              # Core game mechanics
│   ├── constants.py   # Game constants and configurations
│   ├── entities.py    # Robot and task entity definitions
│   └── game.py        # Main game logic
├── ui/                # User interface components
├── utils/             # Utility functions and metrics
└── images/            # Game assets

System Components

1. Task Allocation System

• Market-based auction mechanism
• Priority-based task assignment
• Dynamic task generation
• Waiting time consideration

2. Navigation System

• A* pathfinding algorithm
• Dynamic obstacle avoidance
• Collision prevention between robots
• Path replanning capabilities

3. Performance Monitoring

• Time tracking
• Task completion rate
• Distance measurements

Controls

• Robot: Place robots on the grid
• Obstacle: Add static obstacles
• Task: Create tasks with random priorities
• Random Generate: Automatically generate a random environment
• Play: Start/Pause the simulation
• End: Conclude the simulation and display metrics

Technical Details

• Grid Size: 10x10
• Maximum Tasks: 5
• Maximum Moving Obstacles: 3
• Task Generation Rate: 5% per update
• Obstacle Generation Rate: 2% per update
• Move Delay: 0.5 seconds
• Obstacle Move Delay: 1.0 seconds

Performance Metrics

The system tracks these metrics in real-time:

• Time: Total simulation time in seconds
• Tasks: Number of completed tasks and rate (tasks/second)
• Distance: Total distance traveled and average per task

Requirements

• Python 3.x
• Pygame
• NumPy
• Gym (for RL components)
• Stable-Baselines3 (for DQN implementation)

Usage

1. Clone the repository
2. Install dependencies using pip:

pip install -r requirements.txt

3. Run the main script:

python src/main.py

Implementation Details

The system uses several sophisticated algorithms and techniques:

1. Task Allocation:

   • Auction-based system with bidding mechanism
   • Priority-based task assignment (P1, P2, P3)
   • Distance-based bid calculation

2. Path Planning:

   • A* algorithm for optimal path finding
   • Obstacle detection and avoidance
   • Path recalculation when blocked

3. Reinforcement Learning:

   • DQN for optimizing task allocation
   • Environment based on grid positions
   • Reward system based on distance optimization

4. Performance Tracking:

   • Time measurement
   • Task completion monitoring
   • Distance efficiency calculation

Future Work

Group Tasks:

• Implement tasks that require collaboration among multiple robots, enabling shared effort and parallel contribution to complete complex objectives.

Multi-Task Assignment:

• Allow robots to handle multiple tasks simultaneously, optimizing efficiency and adaptability in dynamic environments.

Weighted Grid Navigation:

• Introduce cell-specific weights to represent difficulty or ease of traversal, such as terrain types or environmental challenges, for more realistic path planning.