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Multi-Agent Warehouse Robot Simulator - Project Structure

📁 Directory Layout

PROJECT/
├── README.md                      # Project overview and features
├── QUICKSTART.md                  # Quick installation and usage guide
├── AI_TECHNIQUES_EXPLAINED.md     # Detailed AI algorithms documentation
├── main.py                        # Main entry point for simulation
├── setup.sh                       # Setup script for installation
├── requirements.txt               # Python dependencies
├── .gitignore                     # Git ignore file
│
└── src/                           # Source code modules
    ├── __init__.py                # Package initialization
    ├── warehouse.py               # Grid environment and package management
    ├── robot.py                   # Autonomous robot agent with AI
    ├── simulator.py               # Simulation orchestration
    ├── visualizer.py              # Matplotlib visualization
    ├── uninformed_search.py       # BFS, DFS, UCS, IDDFS algorithms
    ├── informed_search.py         # A* with multiple heuristics
    ├── local_search.py            # Hill Climbing, Simulated Annealing
    ├── adversarial_search.py      # Minimax, Alpha-Beta, Expectimax
    └── csp.py                     # Constraint Satisfaction for task allocation

Core Modules

warehouse.py

  • Purpose: Manages warehouse grid environment
  • Key Classes: Warehouse, Package, CellType
  • Features:
    • Grid management with obstacles, packages, drop zones
    • Parking zones for idle robots
    • Dynamic package tracking and removal

robot.py

  • Purpose: Autonomous robot agent with AI decision-making
  • Key Classes: Robot, RobotState
  • Features:
    • Multi-state FSM (idle, moving, picking, dropping, parking, blocked)
    • Package priority system (robots WITH packages > robots WITHOUT)
    • Oscillation detection and escape
    • Conflict resolution with priority-based yielding
    • Parking zone management

simulator.py

  • Purpose: Orchestrates multi-robot simulation
  • Key Classes: WarehouseSimulator
  • Features:
    • Multi-robot coordination
    • Package assignment using CSP
    • Statistics tracking
    • Step-by-step execution

visualizer.py

  • Purpose: Real-time visualization
  • Key Classes: WarehouseVisualizer, SimpleVisualizer
  • Features:
    • Matplotlib GUI with grid display
    • Real-time statistics panel (compact format)
    • Color-coded robots, packages, obstacles, drop zones, parking areas

AI Technique Modules

uninformed_search.py

  • Algorithms: BFS, DFS, UCS, IDDFS
  • Use Case: Basic pathfinding, fallback for complex scenarios

informed_search.py

  • Algorithms: A* with Manhattan, Euclidean, Chebyshev heuristics
  • Use Case: Primary pathfinding with optimal paths

local_search.py

  • Algorithms: Hill Climbing, Simulated Annealing
  • Use Case: Path optimization, local improvements

adversarial_search.py

  • Algorithms: Minimax, Alpha-Beta Pruning, Expectimax
  • Use Case: Conflict resolution, multi-agent decision making

csp.py

  • Algorithms: Backtracking with MRV, LCV heuristics
  • Use Case: Optimal package-to-robot task allocation

Entry Points

main.py

  • Primary command-line interface
  • Configurable parameters (grid size, robots, packages, obstacles)
  • Example: python main.py --width 30 --height 30 --robots 10 --packages 20

🔧 Configuration Files

requirements.txt

numpy>=1.24.0
matplotlib>=3.7.0
colorama>=0.4.6

.gitignore

  • Excludes: venv/, __pycache__/, *.pyc, *.pyo, .DS_Store

✨ Key Features Implemented

  1. Package Priority System: Robots with packages get right-of-way
  2. Parking Zones: Idle robots move to designated parking area (5x5 corner)
  3. Interior Parking Priority: Robots fill parking from inside out
  4. Conflict-Free Parking: Robots dynamically choose alternative spots
  5. Oscillation Detection: Tracks position history, escapes loops
  6. Dynamic Obstacle Avoidance: Real-time path replanning around other robots
  7. Drop Zone Congestion Handling: Priority-based waiting for deliveries
  8. Compact Statistics Display: Fits all robot data in visualization panel

🎓 Academic Context

Course: CSL304 - Artificial Intelligence
Institution: IIT Bhilai
Project Type: Multi-Agent System with AI Techniques Integration
Semester: 5 (2024-25)