Technical Implementation: Optimizing Military Vehicle Refueling with AI: My DRDO Internship Project

System Architecture

The system is built using a modular architecture with several key components:

1. Core Components

# Constants and Configuration
MAP_SIZE = 40
DAYS = 15
BASE_FUEL_THRESHOLD = 0.25
REFUEL_AMOUNT = 0.70
STRANDED_FUEL_LEVEL = 10
PATROL_DURATION = 100

# Vehicle Configuration
vehicles = [
    {
        "id": 1,
        "fuel_capacity": 100,
        "consumption_rate": 0.5,
        "position": np.array([10, 10]),
        "fuel_level": 100,
        "path": [[10, 10]],
        "patrol_area": (0, 0, 20, 20),
        "state": "patrolling"
    },
    # Additional vehicles...
]

2. K-means Clustering Implementation

The K-means clustering algorithm optimizes hub placement:

def optimize_hub_locations(vehicle_positions, n_clusters=3):
    # Convert vehicle positions to numpy array
    positions = np.array([pos for pos in vehicle_positions])
    
    # Initialize K-means
    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
    
    # Fit the model and get cluster centers
    kmeans.fit(positions)
    hub_locations = kmeans.cluster_centers_
    
    # Calculate quality metrics
    inertia = kmeans.inertia_
    silhouette_avg = silhouette_score(positions, kmeans.labels_)
    
    return {
        'hub_locations': hub_locations,
        'labels': kmeans.labels_,
        'inertia': inertia,
        'silhouette_score': silhouette_avg
    }

def update_hub_positions(vehicle_history):
    # Collect historical vehicle positions
    all_positions = []
    for vehicle in vehicle_history:
        all_positions.extend(vehicle['positions'])
    
    # Optimize hub locations
    hub_data = optimize_hub_locations(all_positions)
    
    # Update hub positions
    for i, hub in enumerate(hubs):
        hub["position"] = hub_data['hub_locations'][i]
    
    return hub_data['silhouette_score']

3. Genetic Algorithm Implementation

The genetic algorithm optimizes refueling strategies:

def genetic_algorithm(progress_callback=None):
    population_size = 50
    generations = 100
    mutation_rate = 0.1
    crossover_rate = 0.8

    population = [create_individual() for _ in range(population_size)]
    best_fitness = float('-inf')
    best_individual = None

    for generation in range(generations):
        # Evaluate fitness
        fitnesses = [fitness(ind) for ind in population]
        
        # Select parents and create new population
        parents = random.choices(population, weights=fitnesses, k=population_size)
        new_population = []
        
        for i in range(0, population_size, 2):
            child1 = crossover(parents[i], parents[i+1])
            child2 = crossover(parents[i], parents[i+1])

            if random.random() < mutation_rate:
                child1 = mutate(child1)
            if random.random() < mutation_rate:
                child2 = mutate(child2)

            new_population.extend([child1, child2])

        # Elitism: keep the best individual
        best_of_generation = max(population, key=fitness)
        new_population[0] = best_of_generation
        population = new_population

        if progress_callback:
            progress_callback(f"Generation {generation + 1}: Best fitness = {best_fitness}")

    return best_individual, best_fitness

4. Refueling Strategy Implementation

The refueling strategy considers multiple factors:

def apply_refueling_strategy(strategy):
    vehicles_needing_refuel = []
    for vehicle in vehicles:
        nearest_hub = find_nearest_hub(vehicle["position"])
        dynamic_threshold = calculate_dynamic_threshold(vehicle, nearest_hub)
        
        if (vehicle["state"] == "stranded" or
                vehicle["fuel_level"] <= dynamic_threshold * vehicle["fuel_capacity"] or
                predict_refuel_need(vehicle, PATROL_DURATION)):
            vehicles_needing_refuel.append(vehicle)

    if vehicles_needing_refuel:
        # Calculate centroid for efficient refueling
        centroid = np.mean([v["position"] for v in vehicles_needing_refuel], axis=0)
        
        # Optimize refueling sequence
        vehicles_needing_refuel.sort(
            key=lambda v: np.linalg.norm(v["position"] - centroid)
        )
        
        # Handle refueling logistics
        handle_refueling_logistics(vehicles_needing_refuel, centroid)

5. Visualization System

The visualization system provides real-time feedback:

def update(frame, day):
    fig.clear()
    gs = fig.add_gridspec(2, 1, height_ratios=[1, 9])
    ax_dashboard = fig.add_subplot(gs[0], frame_on=False)
    ax_map = fig.add_subplot(gs[1])

    # Update vehicle states
    update_vehicle_states()

    # Draw visualization elements
    plot_entities(ax_map, vehicles, refuel_vehicles, hubs)
    draw_refuel_dashboard(ax_dashboard, vehicles)
    
    # Add alerts and information
    display_alerts(ax_dashboard)
    
    # Update data collection
    frame_data = extract_frame_data(frame, day)
    update_csv(frame_data, day)

6. Data Collection and Analysis

The system includes comprehensive data collection:

def extract_frame_data(frame, day, vehicles, refuel_vehicles, hubs):
    data = []
    
    # Collect vehicle data
    for vehicle in vehicles:
        data.append({
            'frame': frame,
            'day': day,
            'entity_type': 'vehicle',
            'id': vehicle['id'],
            'x_position': vehicle['position'][0],
            'y_position': vehicle['position'][1],
            'fuel_level': vehicle['fuel_level'],
            'state': vehicle['state']
        })
    
    # Collect refuel vehicle data
    for rv in refuel_vehicles:
        data.append({
            'frame': frame,
            'day': day,
            'entity_type': 'refuel_vehicle',
            'id': rv['id'],
            'x_position': rv['position'][0],
            'y_position': rv['position'][1],
            'state': rv['state']
        })
    
    return data

Performance Optimizations

  1. Pathfinding Optimization

    • Implemented efficient graph-based pathfinding
    • Used caching for frequently accessed paths
    • Optimized collision avoidance algorithms

  2. Genetic Algorithm Tuning

    • Balanced population size and generations
    • Implemented adaptive mutation rates
    • Optimized fitness function calculations

  3. Visualization Efficiency

    • Implemented selective rendering
    • Optimized data structure updates
    • Reduced redundant calculations

Future Technical Improvements

  1. Performance Enhancements

    • Implement parallel processing
    • Optimize memory usage
    • Add caching mechanisms
    • Improve batch processing

  2. Visualization Upgrades

    • Add interactive plots
    • Implement 3D visualizations
    • Enhanced comparison tools
    • Real-time updates

  3. Statistical Enhancements

    • Additional statistical methods
    • Advanced validation techniques
    • Improved error handling
    • Enhanced power analysis

The technical implementation demonstrates a robust approach to statistical analysis, combining efficient computation with comprehensive visualization and analysis capabilities.

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