pytorch_disaster_response.py

# Python script for implementing disaster response using the MAGE framework with PyTorch

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import random

# Define the DisasterResponseRobot class to represent individual disaster response robots
class DisasterResponseRobot:
    def __init__(self, robot_id, sensors):
        self.robot_id = robot_id
        self.sensors = sensors
        self.location = None
        self.survey_data = []
        self.log = []

    def move(self, new_location):
        # Move the robot to a new location
        self.location = new_location
        self.log.append(f"Robot {self.robot_id} moved to {new_location}")

    def survey_area(self):
        # Survey the area and collect data using the robot's sensors (simulated)
        data = self.simulate_survey()
        self.survey_data.append(data)
        self.log.append(f"Robot {self.robot_id} surveyed area and collected data: {data}")

    def simulate_survey(self):
        # Simulate the survey process (simulated)
        data = {
            'location': self.location,
            'structural_damage': np.random.choice(['severe', 'moderate', 'minor', 'none'], p=[0.1, 0.3, 0.4, 0.2]),
            'road_accessibility': np.random.choice(['blocked', 'partially_blocked', 'clear'], p=[0.2, 0.3, 0.5]),
            'power_lines': np.random.choice(['damaged', 'intact'], p=[0.4, 0.6]),
            'gas_leaks': np.random.choice(['detected', 'none'], p=[0.3, 0.7]),
            'casualties': np.random.randint(0, 10)
        }
        return data

    def identify_critical_damage(self):
        # Identify critical infrastructure damage based on the survey data
        critical_damage = []
        for data in self.survey_data:
            if data['structural_damage'] == 'severe' or data['gas_leaks'] == 'detected' or data['casualties'] > 5:
                critical_damage.append(data)
        return critical_damage

    def share_data(self):
        # Share survey data with other robots and emergency responders (simulated)
        print(f"Robot {self.robot_id} is sharing survey data...")

    def detailed_log(self):
        # Print detailed log for the robot
        for log_entry in self.log:
            print(log_entry)

# Define the DisasterArea class to represent the disaster-affected area
class DisasterArea:
    def __init__(self, num_robots, area_size):
        self.num_robots = num_robots
        self.area_size = area_size
        self.robots = self.deploy_robots()
        self.steps_log = []

    def deploy_robots(self):
        # Deploy disaster response robots with random sensors
        robots = []
        for i in range(self.num_robots):
            sensors = np.random.choice(['camera', 'lidar', 'gas_sensor', 'thermal_camera'], size=2, replace=False)
            robot = DisasterResponseRobot(i, sensors)
            robots.append(robot)
        return robots

    def coordinate_survey(self, num_steps):
        # Coordinate the robots to survey the disaster-affected area
        for step in range(num_steps):
            step_log = f"Step {step + 1}:\n"
            print(step_log)
            for robot in self.robots:
                # Move the robot to a random location within the area
                new_location = np.random.uniform(0, self.area_size, size=2)
                robot.move(new_location)
                step_log += f"Robot {robot.robot_id} moved to {new_location}\n"

                # Survey the area and collect data
                robot.survey_area()
                step_log += f"Robot {robot.robot_id} collected survey data\n"

                # Share the survey data with other robots and emergency responders
                robot.share_data()
                step_log += f"Robot {robot.robot_id} shared data\n"

            # Identify critical infrastructure damage
            critical_damage = []
            for robot in self.robots:
                critical_damage.extend(robot.identify_critical_damage())

            # Provide real-time information to emergency responders
            self.provide_real_time_info(critical_damage)
            step_log += f"Critical damage identified and shared with responders\n"

            self.steps_log.append(step_log)

    def provide_real_time_info(self, critical_damage):
        # Provide real-time information to emergency responders (simulated)
        print("Providing real-time information to emergency responders:")
        for damage in critical_damage:
            print(f"- Critical damage detected at location {damage['location']}")

    def detailed_logs(self):
        # Print detailed logs for each robot
        for robot in self.robots:
            robot.detailed_log()
        for log in self.steps_log:
            print(log)

# Define a PyTorch Dataset for training a model (if needed)
class DisasterDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        sample = self.data[idx]
        label = self.labels[idx]
        return sample, label

# Define a neural network model for data analysis
class DisasterModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(DisasterModel, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.sigmoid(self.fc2(x))
        return x

# Train the model (dummy training loop)
def train_model(model, dataloader, criterion, optimizer, num_epochs):
    for epoch in range(num_epochs):
        for data, labels in dataloader:
            inputs = data
            targets = labels
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
        print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {loss.item()}")

# Define additional helper functions for disaster response simulation
def analyze_survey_data(data):
    structural_damage = [d['structural_damage'] for d in data]
    road_accessibility = [d['road_accessibility'] for d in data]
    power_lines = [d['power_lines'] for d in data]
    gas_leaks = [d['gas_leaks'] for d in data]
    casualties = [d['casualties'] for d in data]
    return structural_damage, road_accessibility, power_lines, gas_leaks, casualties

def visualize_survey_data(structural_damage, road_accessibility, power_lines, gas_leaks, casualties):
    print("Survey Data Summary:")
    print(f"Structural Damage: {structural_damage}")
    print(f"Road Accessibility: {road_accessibility}")
    print(f"Power Lines: {power_lines}")
    print(f"Gas Leaks: {gas_leaks}")
    print(f"Casualties: {casualties}")

def save_model(model, filepath):
    torch.save(model.state_dict(), filepath)
    print(f"Model saved to {filepath}")

def load_model(model, filepath):
    model.load_state_dict(torch.load(filepath))
    model.eval()
    print(f"Model loaded from {filepath}")

# Main script execution
if __name__ == "__main__":
    # Set the random seed for reproducibility
    torch.manual_seed(42)
    np.random.seed(42)

    # Define the simulation parameters
    num_robots = 5
    area_size = 1000
    num_steps = 10

    # Create an instance of the DisasterArea
    disaster_area = DisasterArea(num_robots, area_size)

    # Coordinate the robots to survey the disaster-affected area
    disaster_area.coordinate_survey(num_steps)

    # Print the log of actions taken during the simulation
    disaster_area.detailed_logs()

    # Dummy data for the dataset
    data = np.random.rand(100, 10)
    labels = np.random.randint(0, 2, size=(100, 1))
    dataset = DisasterDataset(data, labels)
    dataloader = DataLoader(dataset, batch_size=10, shuffle=True)

    # Initialize the model, criterion, and optimizer
    model = DisasterModel(input_size=10, hidden_size=5, output_size=1)
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # Train the model
    train_model(model, dataloader, criterion, optimizer, num_epochs=10)

    # Saving and loading the model
    model_path = "disaster_model.pth"
    save_model(model, model_path)
    load_model(model, model_path)

    # Additional functionality for monitoring and analytics
    all_survey_data = [entry for robot in disaster_area.robots for entry in robot.survey_data]
    structural_damage, road_accessibility, power_lines, gas_leaks, casualties = analyze_survey_data(all_survey_data)
    visualize_survey_data(structural_damage, road_accessibility, power_lines, gas_leaks, casualties)

# Possible Errors and Solutions:
# 1. Error: No module named 'torch'
#    Solution: Make sure you have PyTorch installed. You can install it using pip: `pip install torch`.
#
# 2. Error: ValueError: probabilities do not sum to 1
#    Solution: This error can occur if the probabilities provided to np.random.choice() do not sum to 1.
#              Ensure that the probabilities for each choice sum up to 1.
#              Adjust the probabilities or normalize them if necessary.
#
# 3. Error: KeyError: 'location'
#    Solution: This error can happen if the 'location' key is not present in the critical_damage dictionary.
#              Make sure to include the 'location' key in the survey data dictionary.
#              Update the simulate_survey() method to include the 'location' key.
#
# 4. Error: Inefficient area coverage or coordination
#    Solution: If the area coverage or coordination of robots is not efficient, consider implementing more advanced algorithms.
#              For area coverage, you can use techniques like grid-based search, spiral search, or frontier-based exploration.
#              For coordination, you can implement task allocation algorithms or use communication protocols to share information efficiently.
#              Continuously monitor the performance and adapt the strategies based on the real-time situation.

# Note: The code provided here is a simplified simulation and may not cover all the complexities of real-world disaster response.
#       In practice, you would need to integrate with the actual robot control systems, sensor data processing, and communication protocols
#       to deploy the multi-agent system effectively. The simulation can be extended to incorporate more realistic disaster scenarios,
#       heterogeneous robot capabilities, and coordination with human responders. Additionally, data from satellite imagery, GIS systems,
#       and on-ground reports can be used to enhance the situational awareness and decision-making processes.