🏢 3D Multi-Robot Rescue Simulation

A real-time simulation of autonomous rescue robots navigating a 3D building to locate and rescue survivors, optimized using a Genetic Algorithm (GA) with A pathfinding*.

🎯 Overview

This project simulates a disaster rescue scenario where multiple robots must:

• Navigate a 3D grid-based building with obstacles
• Detect survivors using simulated heat and CO2 sensors
• Compute optimal rescue paths while minimizing risk and travel distance
• Coordinate to avoid redundant assignments

The Genetic Algorithm evolves rescue plans over generations, optimizing both:

1. Survivor Assignment — Which robot rescues which survivors
2. Visit Sequence — The order each robot visits its assigned survivors

🧬 Genetic Algorithm

Component	Implementation
Chromosome	Robot positions + ordered survivor sequences
Selection	Tournament selection (size 3)
Crossover	Single-point crossover on robot assignments
Mutation	Swap, Remove, Transfer, Reverse operations
Fitness	Maximizes rescues, minimizes path length & risk
Elitism	Top 10% preserved each generation

⚡ Parallel Processing (IPC)

Fitness evaluation is parallelized using POSIX IPC:

• Shared Memory (shm_open + mmap) — Zero-copy data sharing between processes
• Semaphores — Work distribution and synchronization
• Process Pool (fork) — Worker processes compute fitness in parallel

🗺️ Pathfinding

• A* Algorithm for optimal 3D pathfinding
• 6-directional movement (±X, ±Y, ±Z)
• Risk-aware path cost calculation
• Handles obstacles and blocked cells

🖥️ Visualization

Real-time OpenGL/GLUT 3D visualization showing:

• Building structure with obstacles
• Survivor locations
• Robot paths and movements
• Risk heatmap

📁 Project Structure

├── include/           # Header files (.h)
├── src/               # Source files (.c)
│   ├── main.c         # Entry point
│   ├── ga.c           # Genetic Algorithm
│   ├── ga_parallel.c  # Parallel processing (IPC)
│   ├── astar.c        # A* Pathfinding
│   ├── grid.c         # 3D Grid management
│   ├── config.c       # Config parser
│   └── visualize.c    # OpenGL visualization
├── build/             # Object files
├── configfile.txt     # Parameters
└── Makefile           # Build script

⚙️ Configuration

Edit configfile.txt to customize:

GRID_X = 20              # Building dimensions
GRID_Y = 20
GRID_Z = 20
OBSTACLE_DENSITY = 0.3   # 30% obstacles
ROBOT_COUNT = 8          # Number of rescue robots
POPULATION_SIZE = 100    # GA population
GENERATIONS = 200        # Evolution iterations
MUTATION_RATE = 0.3      # 30% mutation chance
POOL_SIZE = 4            # Parallel worker processes

Dependencies

• GCC compiler
• OpenGL & GLUT libraries
• POSIX-compliant system (Linux/WSL)

📊 Output

The program compares:

1. Optimal A* Solution — Exhaustive/greedy baseline
2. GA Solution — Evolved rescue plan

🎓 Key Concepts Demonstrated

• Genetic Algorithms for combinatorial optimization
• A* pathfinding in 3D space
• Inter-Process Communication (IPC) with shared memory
• Process synchronization with semaphores
• Real-time 3D visualization with OpenGL