pytorch_manufacturing_assembly.py

# Python script for implementing collaborative assembly 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 AssemblyRobot class to represent individual assembly robots
class AssemblyRobot:
    def __init__(self, robot_id, capabilities):
        self.robot_id = robot_id
        self.capabilities = capabilities
        self.location = np.array([0.0, 0.0, 0.0])
        self.assigned_task = None
        self.task_history = []

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

    def assign_task(self, task):
        # Assign an assembly task to the robot
        self.assigned_task = task
        self.task_history.append(task)
        print(f"Robot {self.robot_id} assigned task: {task['name']}")

    def perform_task(self):
        # Perform the assigned assembly task (simulated)
        if self.assigned_task is not None:
            print(f"Robot {self.robot_id} is performing task: {self.assigned_task['name']}")
            success_prob = np.random.uniform(0.8, 1.0)  # Simulated task success probability
            success = np.random.choice([True, False], p=[success_prob, 1 - success_prob])
            if success:
                print(f"Robot {self.robot_id} successfully completed task: {self.assigned_task['name']}")
            else:
                print(f"Robot {self.robot_id} failed to complete task: {self.assigned_task['name']}")
            self.assigned_task = None
            return success
        return False

    def share_information(self, assembly_info):
        # Share information about part locations and orientations with other robots (simulated)
        print(f"Robot {self.robot_id} is sharing assembly information: {assembly_info}")

    def log_task_history(self):
        # Print detailed log of tasks performed by the robot
        for task in self.task_history:
            print(f"Robot {self.robot_id} performed task: {task['name']}")

# Define the ManufacturingPlant class to represent the manufacturing plant environment
class ManufacturingPlant:
    def __init__(self, num_robots, assembly_tasks):
        self.num_robots = num_robots
        self.assembly_tasks = assembly_tasks
        self.robots = self.deploy_robots()
        self.steps_log = []

    def deploy_robots(self):
        # Deploy assembly robots with random capabilities
        robots = []
        for i in range(self.num_robots):
            capabilities = np.random.choice(['drilling', 'welding', 'painting', 'fastening'], size=2, replace=False)
            robot = AssemblyRobot(i, capabilities)
            robots.append(robot)
        return robots

    def assign_tasks_to_robots(self):
        # Assign assembly tasks to robots based on their capabilities
        for task in self.assembly_tasks:
            capable_robots = [robot for robot in self.robots if task['capability'] in robot.capabilities]
            if capable_robots:
                assigned_robot = np.random.choice(capable_robots)
                assigned_robot.assign_task(task)
            else:
                print(f"No capable robot found for task: {task['name']}")

    def coordinate_assembly(self):
        # Coordinate the assembly process among robots
        assembly_info = self.generate_assembly_info()
        for robot in self.robots:
            robot.share_information(assembly_info)
            success = robot.perform_task()
            if not success:
                print(f"Robot {robot.robot_id} failed to complete its task.")

    def generate_assembly_info(self):
        # Generate simulated assembly information (e.g., part locations, orientations)
        num_parts = len(self.assembly_tasks)
        part_locations = np.random.uniform(0, 10, size=(num_parts, 3))
        part_orientations = np.random.uniform(0, 360, size=num_parts)
        assembly_info = {
            'part_locations': part_locations,
            'part_orientations': part_orientations
        }
        return assembly_info

    def optimize_assembly(self):
        # Optimize the assembly process based on the shared information and task performance (simulated)
        print("Optimizing the assembly process...")

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

# Define a PyTorch Dataset for training a model (if needed)
class AssemblyDataset(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 AssemblyModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(AssemblyModel, 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 assembly simulation
def analyze_task_performance(task_history):
    completed_tasks = [task['name'] for task in task_history]
    return completed_tasks

def visualize_task_performance(completed_tasks):
    print("Task Performance Summary:")
    for task in completed_tasks:
        print(f"Completed Task: {task}")

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 assembly tasks
    assembly_tasks = [
        {'name': 'Assemble Part A', 'capability': 'drilling'},
        {'name': 'Weld Component B', 'capability': 'welding'},
        {'name': 'Paint Surface C', 'capability': 'painting'},
        {'name': 'Fasten Part D', 'capability': 'fastening'}
    ]

    # Create an instance of the ManufacturingPlant
    num_robots = 4
    plant = ManufacturingPlant(num_robots, assembly_tasks)

    # Assign tasks to robots and coordinate the assembly process
    plant.assign_tasks_to_robots()
    plant.coordinate_assembly()
    plant.optimize_assembly()

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

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

    # Initialize the model, criterion, and optimizer
    model = AssemblyModel(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 = "assembly_model.pth"
    save_model(model, model_path)
    load_model(model, model_path)

    # Additional functionality for monitoring and analytics
    task_histories = [robot.task_history for robot in plant.robots]
    completed_tasks = [task for history in task_histories for task in analyze_task_performance(history)]
    visualize_task_performance(completed_tasks)

# 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: IndexError: index 3 is out of bounds for axis 0 with size 3
#    Solution: This error can occur if the number of dimensions in the `part_locations` array does not match the expected size.
#              Make sure that the size of `part_locations` is consistent with the number of parts and dimensions.
#              Adjust the size of `part_locations` in the `generate_assembly_info` method accordingly.
#
# 3. Error: KeyError: 'capability'
#    Solution: This error can happen if the 'capability' key is not present in the assembly task dictionary.
#              Ensure that each task in the `assembly_tasks` list has a 'capability' key specifying the required capability.
#              Double-check the task definitions and make sure they include the 'capability' key.
#
# 4. Error: No capable robot found for a task
#    Solution: This error occurs when there is no robot with the required capability to perform a specific assembly task.
#              Consider increasing the number of robots or assigning additional capabilities to the existing robots.
#              Modify the `deploy_robots` method to assign capabilities based on the specific requirements of the assembly tasks.

# Note: The code provided here is a simplified simulation and may not cover all the complexities of real-world manufacturing assembly.
#       In practice, you would need to integrate with the actual robot control systems, sensor data, and manufacturing execution systems (MES)
#       to deploy the multi-agent system effectively. The simulation can be extended to incorporate more realistic assembly scenarios,
#       task dependencies, resource constraints, and optimization algorithms based on real-time data from the manufacturing process.
#       Assembly information, such as part locations and orientations, would typically be obtained from computer-aided design (CAD) models,
#       vision systems, or other sensing mechanisms in a real-world setting.