Automated Guided Vehicle System for Warehouse Logistics Optimization MATLAB

Okay, let's break down the AGV system for warehouse logistics optimization, focusing on the MATLAB code structure, operational logic, and real-world implementation considerations.  I'll provide a high-level code outline and prioritize the essential concepts for a functional system.

**Project Overview: Automated Guided Vehicle (AGV) System for Warehouse Logistics**

This project aims to design and simulate an AGV system using MATLAB to optimize material flow within a warehouse environment.  The system will involve multiple AGVs that autonomously navigate the warehouse, pick up items from designated locations, and deliver them to specified destinations.  The goal is to minimize travel time, reduce human intervention, and improve overall warehouse efficiency.

**I.  MATLAB Code Structure and Logic**

Here's a breakdown of the core MATLAB components and their functionalities.  Note that this is a simplified structure. A real-world system would likely be more complex and modular.

```matlab
% ------------------------------------------------------------
% Main Script: agv_warehouse_simulation.m
% ------------------------------------------------------------

% 1.  Warehouse Setup and Initialization
warehouse_layout = create_warehouse_layout(); % Defines warehouse dimensions, aisles, storage locations
agv_specs = define_agv_specifications();    % AGV parameters (speed, load capacity, turning radius, etc.)
agv_start_locations = initialize_agv_locations(warehouse_layout, num_agvs); % Initial AGV starting positions
agv_states = initialize_agv_states(num_agvs); % AGV states (idle, moving, loading, unloading, error)
task_queue = initialize_task_queue();        % Task requests (pickup location, delivery location)

% 2.  Main Simulation Loop
simulation_time = 0;
simulation_duration = 3600; % Simulate for 1 hour (seconds)
dt = 1; % Time step (seconds)

while simulation_time < simulation_duration

  % 3.  Task Assignment (Centralized or Decentralized)
  [agv_states, task_queue, agv_assignments] = assign_tasks(agv_states, task_queue, agv_start_locations);

  % 4.  AGV Path Planning and Navigation
  for i = 1:num_agvs
    if agv_states(i).current_task_id ~= 0 % If AGV has a task
      [path, success] = plan_path(agv_start_locations(i,:), task_queue(agv_states(i).current_task_id).pickup_location, warehouse_layout);
      if success
         [agv_states(i)] = navigate_agv(agv_start_locations(i,:),path,agv_specs,dt,warehouse_layout);
         agv_start_locations(i,:) = agv_states(i).location;

          %Logic for checking reach location
          if(check_agv_reach_destination(agv_start_locations(i,:), task_queue(agv_states(i).current_task_id).pickup_location,1))
            agv_states(i).state = "Loading";
            pause(5); % loading duration
            [path, success] = plan_path(agv_start_locations(i,:), task_queue(agv_states(i).current_task_id).dropoff_location, warehouse_layout);

            if success
                [agv_states(i)] = navigate_agv(agv_start_locations(i,:),path,agv_specs,dt,warehouse_layout);
                agv_start_locations(i,:) = agv_states(i).location;
                if(check_agv_reach_destination(agv_start_locations(i,:), task_queue(agv_states(i).current_task_id).dropoff_location,1))
                    agv_states(i).state = "Idle";
                    task_queue(agv_states(i).current_task_id).status = "Completed";
                    agv_states(i).current_task_id = 0;

                end
            end

          end

      else
        % Handle path planning failure (e.g., re-plan, mark task as failed)
        disp(['AGV ', num2str(i), ': Path planning failed.']);
        agv_states(i).state = "Error";
        task_queue(agv_states(i).current_task_id).status = "Failed";
      end
    end
  end

  % 5.  Collision Avoidance
  agv_states = collision_avoidance(agv_states, warehouse_layout, agv_specs, dt);

  % 6.  Warehouse Operations (Loading/Unloading) - Placeholder
  %  Implement logic to simulate loading and unloading times.

  % 7.  Data Logging and Visualization (Optional)
  log_data(simulation_time, agv_states, task_queue);
  visualize_simulation(warehouse_layout, agv_states, task_queue, simulation_time);

  simulation_time = simulation_time + dt;
  pause(0.1); % add a small delay
end

% 8.  Performance Evaluation
analyze_performance(agv_states, task_queue, simulation_time);

% ------------------------------------------------------------
% Function: create_warehouse_layout()
% ------------------------------------------------------------
function warehouse_layout = create_warehouse_layout()
% Defines the warehouse environment (dimensions, obstacles, storage locations).
% This could be a matrix representing the warehouse grid, with values
% indicating free space, obstacles, storage locations, etc.
  warehouse_layout.width = 50;  % Example warehouse dimensions
  warehouse_layout.height = 30;
  warehouse_layout.aisle_width = 3;
  warehouse_layout.storage_locations = [5, 5; 10, 15; 40, 25]; % Example storage locations (x, y)
  warehouse_layout.obstacles = [15, 5, 5, 2];  % Example obstacle: [x, y, width, height]
end

% ------------------------------------------------------------
% Function: define_agv_specifications()
% ------------------------------------------------------------
function agv_specs = define_agv_specifications()
  agv_specs.max_speed = 2;     % meters per second
  agv_specs.load_capacity = 100;  % kg
  agv_specs.turning_radius = 1;   % meters
  agv_specs.wheelbase = 0.5;      % meters (for kinematic models)
end

% ------------------------------------------------------------
% Function: initialize_agv_locations()
% ------------------------------------------------------------
function agv_start_locations = initialize_agv_locations(warehouse_layout, num_agvs)
  %  Randomly distribute AGVs in safe areas of the warehouse.
  agv_start_locations = zeros(num_agvs, 2);
  for i = 1:num_agvs
    agv_start_locations(i,:) = [randi([1, warehouse_layout.width]), randi([1, warehouse_layout.height])]; % Random locations
    %  Add checks to ensure AGVs don't start inside obstacles or storage locations.
  end
end

% ------------------------------------------------------------
% Function: initialize_agv_states()
% ------------------------------------------------------------
function agv_states = initialize_agv_states(num_agvs)
  % Defines the state of each AGV (idle, moving, loading, etc.)
  for i = 1:num_agvs
    agv_states(i).id = i;
    agv_states(i).state = "Idle"; % "Idle", "Moving", "Loading", "Unloading", "Error"
    agv_states(i).current_task_id = 0; % ID of the task the AGV is currently assigned to
    agv_states(i).location = [0,0];
    agv_states(i).velocity = [0, 0];
  end
end

% ------------------------------------------------------------
% Function: initialize_task_queue()
% ------------------------------------------------------------
function task_queue = initialize_task_queue()
  % Creates a queue of tasks for the AGVs.  Could be randomly generated
  % or read from a file.
  num_tasks = 10; % Example: 10 tasks
  for i = 1:num_tasks
    task_queue(i).id = i;
    task_queue(i).pickup_location = [randi([1, 50]), randi([1, 30])]; % Random pickup location
    task_queue(i).dropoff_location = [randi([1, 50]), randi([1, 30])]; % Random dropoff location
    task_queue(i).status = "Pending"; % "Pending", "Assigned", "InProgress", "Completed", "Failed"
    task_queue(i).priority = randi([1, 5]);  % Example: Priority level (1-5)
  end
end

% ------------------------------------------------------------
% Function: assign_tasks()
% ------------------------------------------------------------
function [agv_states, task_queue, agv_assignments] = assign_tasks(agv_states, task_queue,agv_start_locations)
  %  Assigns tasks from the task queue to available AGVs.
  %  This could be a simple nearest-neighbor assignment or a more sophisticated
  %  optimization algorithm (e.g., Hungarian algorithm).
  num_agvs = length(agv_states);
  num_tasks = length(task_queue);
  agv_assignments = zeros(1, num_agvs); % AGV assignments

  %Simple Assign Task Algorithm
    for i = 1:num_agvs
        if (strcmp(agv_states(i).state,"Idle"))

            % Find the nearest unassigned task
            min_distance = Inf;
            nearest_task_id = 0;
            for j = 1:num_tasks
                if strcmp(task_queue(j).status, "Pending")
                  distance = norm(agv_start_locations(i,:) - task_queue(j).pickup_location);
                    if distance < min_distance
                        min_distance = distance;
                        nearest_task_id = j;
                    end
                end
            end

            % Assign the task to the AGV
            if nearest_task_id ~= 0
                agv_states(i).state = "Assigned";
                agv_states(i).current_task_id = nearest_task_id;
                task_queue(nearest_task_id).status = "Assigned";
                agv_assignments(i) = nearest_task_id;

            end
        end
    end
end

% ------------------------------------------------------------
% Function: plan_path()
% ------------------------------------------------------------
function [path, success] = plan_path(start_location, goal_location, warehouse_layout)
  %  Plans a path for the AGV to navigate from the start to the goal location,
  %  avoiding obstacles.  A* algorithm is a common choice.
  %  Returns: path (list of waypoints), success (boolean indicating if a path was found)

  % Simple straight line path (replace with A* or other path planner)
  path = [start_location; goal_location];
  success = true; % Assume path planning is always successful for this example
end

% ------------------------------------------------------------
% Function: navigate_agv()
% ------------------------------------------------------------
function [agv_states] = navigate_agv(start_location, path, agv_specs, dt, warehouse_layout)
  %  Simulates the AGV's movement along the planned path.
  %  Considers AGV dynamics (speed, turning radius).
    agv_states.location = path(2,:);
  %  Implement kinematic or dynamic model of the AGV.

end
function reach = check_agv_reach_destination(agv_location,destination_location,tolerance)
  reach = norm(agv_location - destination_location) < tolerance;
end
% ------------------------------------------------------------
% Function: collision_avoidance()
% ------------------------------------------------------------
function agv_states = collision_avoidance(agv_states, warehouse_layout, agv_specs, dt)
  %  Detects potential collisions between AGVs and adjusts their paths or speeds
  %  to avoid them.
    num_agvs = length(agv_states);

    for i = 1:num_agvs
      for j = i+1:num_agvs

            distance = norm(agv_states(i).location - agv_states(j).location);
            if (distance < agv_specs.turning_radius)
                agv_states(i).velocity = [0,0];
                agv_states(j).velocity = [0,0];
                disp("Collision Avoidance")
            end
      end
    end
end
% ------------------------------------------------------------
% Function: log_data()
% ------------------------------------------------------------
function log_data(simulation_time, agv_states, task_queue)
  %  Logs simulation data for analysis.
  %  Could write to a file or store in MATLAB variables.
    disp(['Simulation Time: ', num2str(simulation_time)]);
end

% ------------------------------------------------------------
% Function: visualize_simulation()
% ------------------------------------------------------------
function visualize_simulation(warehouse_layout, agv_states, task_queue, simulation_time)
  %  Visualizes the warehouse environment, AGV positions, and task status.
  %  Uses MATLAB plotting functions.
    clf;  % Clear the current figure
    hold on;

    % Plot Warehouse Layout
    rectangle('Position', [0, 0, warehouse_layout.width, warehouse_layout.height], 'EdgeColor', 'k');

    % Plot Obstacles
    for i = 1:size(warehouse_layout.obstacles, 1)
        obstacle = warehouse_layout.obstacles(i, :);
        rectangle('Position', [obstacle(1), obstacle(2), obstacle(3), obstacle(4)], 'FaceColor', 'k');
    end

    % Plot Storage Locations
    for i = 1:size(warehouse_layout.storage_locations, 1)
        plot(warehouse_layout.storage_locations(i, 1), warehouse_layout.storage_locations(i, 2), 'rs', 'MarkerSize', 8, 'MarkerFaceColor', 'r');
    end

    % Plot AGVs
    num_agvs = length(agv_states);
    for i = 1:num_agvs
        if strcmp(agv_states(i).state, "Idle")
            color = 'b'; % Blue for Idle
        elseif strcmp(agv_states(i).state, "Moving")
            color = 'g'; % Green for Moving
        elseif strcmp(agv_states(i).state, "Loading")
            color = 'y'; % Yellow for Loading
        elseif strcmp(agv_states(i).state, "Unloading")
            color = 'm'; % Magenta for Unloading
        else
            color = 'r'; % Red for Error or Assigned
        end
        plot(agv_states(i).location(1), agv_states(i).location(2), 'o', 'MarkerSize', 10, 'MarkerFaceColor', color);
        text(agv_states(i).location(1) + 1, agv_states(i).location(2) + 1, num2str(i)); % AGV ID
    end

    % Plot Task Queue (optional - just show pickup/dropoff)
    for i = 1:length(task_queue)
      if strcmp(task_queue(i).status, "Pending")
        plot(task_queue(i).pickup_location(1), task_queue(i).pickup_location(2), 'k^', 'MarkerSize', 6); % Pickup (Black Triangle)
        plot(task_queue(i).dropoff_location(1), task_queue(i).dropoff_location(2), 'kv', 'MarkerSize', 6); % Dropoff (Black Down Triangle)
      end
    end

    title(['AGV Warehouse Simulation - Time: ', num2str(simulation_time)]);
    axis([0 warehouse_layout.width 0 warehouse_layout.height]);
    axis equal;
    grid on;
    hold off;
    drawnow;
end

% ------------------------------------------------------------
% Function: analyze_performance()
% ------------------------------------------------------------
function analyze_performance(agv_states, task_queue, simulation_time)
  %  Calculates and displays performance metrics (e.g., throughput, average task completion time, AGV utilization).
  num_tasks = length(task_queue);
    completed_tasks = sum(strcmp({task_queue.status}, "Completed"));
    failed_tasks = sum(strcmp({task_queue.status}, "Failed"));

    throughput = completed_tasks / simulation_time; % Tasks per second
    total_task_time = 0;
    for i = 1:num_tasks
        if strcmp(task_queue(i).status, "Completed")
            total_task_time = total_task_time + task_queue(i).end_time - task_queue(i).start_time; %This needs to be implemented in assign task to keep track
        end
    end
    avg_task_completion_time = total_task_time / completed_tasks;

    agv_utilization = 0;
    for i = 1:length(agv_states)
        agv_utilization = agv_utilization + agv_states(i).time_busy / simulation_time; %This needs to be implemented in assign task to keep track
    end
    agv_utilization = agv_utilization / length(agv_states);

    disp('--- Performance Analysis ---');
    disp(['Throughput: ', num2str(throughput), ' tasks/second']);
    disp(['Average Task Completion Time: ', num2str(avg_task_completion_time), ' seconds']);
    disp(['AGV Utilization: ', num2str(agv_utilization * 100), '%']);
    disp(['Completed Tasks: ', num2str(completed_tasks)]);
    disp(['Failed Tasks: ', num2str(failed_tasks)]);
end

```

**Key Functions and Concepts:**

*   **`create_warehouse_layout()`:** Defines the warehouse environment.  Crucially includes dimensions, obstacle locations, and storage location coordinates.  This needs to be accurate for path planning and collision avoidance.
*   **`define_agv_specifications()`:**  Sets parameters for the AGVs, like speed, load capacity, and turning radius.  These influence realistic simulation behavior.
*   **`initialize_agv_locations()`:** Places AGVs at starting locations.  Avoid placing them inside obstacles.
*   **`initialize_agv_states()`:** Sets initial states of the AGVs (e.g., 'Idle').  This is critical for task assignment logic.
*   **`initialize_task_queue()`:** Creates a list of tasks with pickup and delivery locations.  This simulates the demand for material movement.
*   **`assign_tasks()`:** Assigns tasks to AGVs based on some strategy (e.g., nearest available AGV).  This is a core optimization component.  More advanced strategies could consider task priorities and AGV capabilities.
*   **`plan_path()`:**  Calculates a path from the AGV's current location to the task's pickup location.  A* algorithm or similar pathfinding is recommended.
*   **`navigate_agv()`:**  Simulates the AGV moving along the planned path, considering its speed and turning radius.  This could involve a simple kinematic model.
*   **`collision_avoidance()`:** Detects potential collisions between AGVs and adjusts their paths or speeds.  This is crucial for multi-AGV systems.
*   **`log_data()`:** Records simulation data for analysis (e.g., AGV positions, task completion times).
*   **`visualize_simulation()`:**  Creates a visual representation of the warehouse, AGVs, and tasks using MATLAB plotting.  This helps debug and understand the simulation.
*   **`analyze_performance()`:**  Calculates metrics to evaluate the system's performance (e.g., throughput, AGV utilization).

**II.  Operational Logic**

1.  **Initialization:** The simulation starts by setting up the warehouse environment, defining AGV characteristics, and creating a task queue.

2.  **Task Assignment:**  AGVs are assigned tasks from the queue based on a chosen algorithm.  A simple approach is to assign the nearest idle AGV to the highest-priority pending task.

3.  **Path Planning:**  Once a task is assigned, the AGV plans a path to the pickup location, avoiding obstacles.

4.  **Navigation:**  The AGV follows the planned path, updating its position at each time step.

5.  **Loading/Unloading:** When the AGV reaches the pickup location, it simulates loading the item.  After loading, it plans a path to the drop-off location and navigates there.  At the drop-off location, it simulates unloading.

6.  **Collision Avoidance:** At each time step, the system checks for potential collisions between AGVs and adjusts their speeds or paths to prevent them.

7.  **Data Logging and Visualization:** Throughout the simulation, data is logged for later analysis, and the simulation is visualized to provide a real-time view of the AGV movements.

8.  **Performance Evaluation:** After the simulation completes, performance metrics are calculated and displayed to assess the system's efficiency.

**III. Real-World Implementation Details and Considerations**

To transition this MATLAB simulation into a real-world AGV system, you need to address several key challenges:

1.  **AGV Hardware:**

    *   **AGV Selection:** Choose AGVs that meet the warehouse's requirements in terms of load capacity, speed, navigation technology, and safety features.  Common AGV types include:
        *   **Automated Forklifts:** For pallet handling.
        *   **Unit Load Carriers:** For transporting individual items or containers.
        *   **Towing Vehicles:** For pulling carts.
        *   **Autonomous Mobile Robots (AMRs):** More flexible navigation than traditional AGVs.
    *   **Sensors:**
        *   **Lidar (Light Detection and Ranging):** For mapping the environment and detecting obstacles.
        *   **Cameras:** For visual navigation, object recognition, and barcode/QR code scanning.
        *   **Inertial Measurement Units (IMUs):** For measuring orientation and acceleration.
        *   **Encoders:** For measuring wheel rotation and distance traveled.
    *   **Actuators:** Motors to drive the wheels and steering mechanisms.
    *   **Communication:** Wireless communication (Wi-Fi, Bluetooth, or dedicated industrial protocols) for communication with the central control system and other AGVs.
    *   **Safety Systems:** Emergency stop buttons, safety bumpers, and laser scanners to prevent collisions with people and objects.
    *   **Power Supply:** Batteries with sufficient capacity for a full shift of operation.  Consider automatic charging systems.

2.  **Navigation and Localization:**

    *   **Navigation Technology:**
        *   **Laser Guidance:** AGV follows a laser beam reflected from reflectors placed around the warehouse.  Accurate but less flexible.
        *   **Magnetic Tape Guidance:** AGV follows a magnetic tape laid on the floor.  Simple but inflexible.
        *   **Wire Guidance:** AGV follows a wire embedded in the floor.  Reliable but expensive to install.
        *   **Inertial Navigation:** AGV uses IMUs to track its position.  Requires periodic recalibration.
        *   **Vision-Based Navigation:** AGV uses cameras to recognize landmarks and navigate.  Flexible but can be affected by lighting conditions.
        *   **SLAM (Simultaneous Localization and Mapping):** AGV builds a map of the environment and simultaneously uses the map to localize itself.  Highly flexible.
    *   **Localization:** Determining the AGV's precise location within the warehouse.  This can be achieved using:
        *   **Beacons:** Fixed transmitters that emit signals that the AGV can use to determine its position.
        *   **QR Codes/Barcodes:** AGV scans codes placed around the warehouse to identify its location.
        *   **Natural Feature Recognition:** AGV uses cameras to identify and track natural features in the environment.

3.  **Warehouse Infrastructure:**

    *   **Clear Pathways:** Ensure clear and unobstructed pathways for the AGVs to navigate.
    *   **Floor Quality:** The floor should be smooth and level to ensure reliable AGV operation.
    *   **Charging Stations:** Install charging stations at strategic locations in the warehouse.
    *   **Wireless Network:** A robust and reliable wireless network is essential for communication between the AGVs and the central control system.
    *   **Safety Barriers:** Physical barriers may be needed to separate AGV traffic from pedestrian traffic.

4.  **Control System and Software:**

    *   **Warehouse Management System (WMS) Integration:** The AGV system must integrate with the WMS to receive task orders and update inventory information.
    *   **Task Management:** A central system to manage task assignment, prioritization, and routing.  This system needs to:
        *   Receive task requests from the WMS.
        *   Assign tasks to available AGVs based on location, availability, and task priority.
        *   Monitor the progress of each task.
        *   Handle exceptions, such as AGV breakdowns or task failures.
    *   **Traffic Management:** A system to coordinate the movement of multiple AGVs and prevent collisions.  This system needs to:
        *   Track the location of each AGV in real-time.
        *   Plan routes for AGVs that avoid congestion and collisions.
        *   Dynamically adjust routes as needed based on changing conditions.
    *   **AGV Control Software:** Software running on the AGVs to control their movement, navigation, and interaction with other systems.  This software needs to:
        *   Receive commands from the central control system.
        *   Control the AGV's motors and steering mechanisms.
        *   Process sensor data to navigate and avoid obstacles.
        *   Communicate with other AGVs and the central control system.
    *   **Simulation and Testing:** Extensive simulation and testing are crucial before deploying the AGV system in the real world.  This helps to identify and resolve potential problems.
    *   **User Interface:** A user-friendly interface for monitoring the AGV system and managing tasks.

5.  **Safety:**

    *   **Safety Standards Compliance:**  Adhere to relevant safety standards for AGV systems (e.g., ANSI B56.5, ISO 3691-4).
    *   **Risk Assessment:** Conduct a thorough risk assessment to identify potential hazards and implement appropriate safety measures.
    *   **Training:** Provide comprehensive training for all personnel who will be working with the AGV system.

6.  **Maintenance:**

    *   **Preventive Maintenance:** Regular maintenance to ensure the AGVs are operating correctly and to prevent breakdowns.
    *   **Spare Parts Inventory:** Maintain an inventory of spare parts to minimize downtime in case of breakdowns.
    *   **Technical Support:**  Have access to technical support from the AGV vendor or a qualified service provider.

**Detailed Examples of Code Adaptations for Real-World Scenarios**

*   **Path Planning (More Realistic):**

    ```matlab
    %  Instead of straight lines, use A* or Dijkstra's algorithm
    %  Consider:
    %   - Grid representation of the warehouse.
    %   - Cost associated with each grid cell (distance, obstacle avoidance).
    %   - Heuristic function (for A*) to estimate distance to the goal.

    function [path, success] = plan_path(start_location, goal_location, warehouse_layout)
      % Example using A* (requires a function a_star_search)
      grid = create_grid_from_layout(warehouse_layout);
      [path_indices, success] = a_star_search(grid, start_location, goal_location);

      if success
        % Convert grid indices to actual coordinates
        path = indices_to_coordinates(path_indices, warehouse_layout);
      else
        path = [];
      end
    end

    function grid = create_grid_from_layout(warehouse_layout)
      %  Create a grid representation of the warehouse,
      %  marking obstacles as impassable.
      grid = ones(warehouse_layout.height, warehouse_layout.width); % 1 = free

      % Mark obstacles
      for i = 1:size(warehouse_layout.obstacles, 1)
        obstacle = warehouse_layout.obstacles(i, :);
        x_start = max(1, floor(obstacle(1)));
        y_start = max(1, floor(obstacle(2)));
        x_end = min(warehouse_layout.width, ceil(obstacle(1) + obstacle(3)));
        y_end = min(warehouse_layout.height, ceil(obstacle(2) + obstacle(4)));
        grid(y_start:y_end, x_start:x_end) = 0; % 0 = obstacle
      end
    end

    % (Implement a_star_search function separately - it's a standard algorithm)
    ```

*   **Navigation (Kinematic Model):**

    ```matlab
    function [agv_states] = navigate_agv(start_location, path, agv_specs, dt, warehouse_layout)
    % Simplified kinematic model (constant speed)

      distance_to_waypoint = norm(path(2,:) - start_location);
      time_to_waypoint = distance_to_waypoint / agv_specs.max_speed;

      if time_to_waypoint <= dt % Can reach waypoint in this time step
        agv_states.location = path(2,:); % Move directly to waypoint
      else
        % Move towards the waypoint
        direction = (path(2,:) - start_location) / distance_to_waypoint;
        agv_states.location = start_location + direction * agv_specs.max_speed * dt;
      end
    end

    ```

*   **Collision Avoidance (Velocity Obstacles):**

    ```matlab
    function agv_states = collision_avoidance(agv_states, warehouse_layout, agv_specs, dt)
    %  Simplified collision avoidance using velocity obstacles.

      num_agvs = length(agv_states);
      for i = 1:num_agvs
        for j = i+1:num_agvs
          % 1. Predict future positions (assuming constant velocity)
          predicted_position_i = agv_states(i).location + agv_states(i).velocity * dt;
          predicted_position_j = agv_states(j).location + agv_states(j).velocity * dt;

          % 2. Check for collision (within a safety radius)
          distance = norm(predicted_position_i - predicted_position_j);
          safety_radius = agv_specs.turning_radius * 2; % Example

          if distance < safety_radius
            % 3. Adjust velocity to avoid collision (simplistic)
            %  Reduce speed or change direction slightly.
            %  More sophisticated methods involve velocity obstacle calculations.

            % Simple: Stop both AGVs
            agv_states(i).velocity = [0, 0];
            agv_states(j).velocity = [0, 0];
            disp(['AGV ', num2str(i), ' and AGV ', num2str(j), ': Collision avoidance activated.']);
          end
        end
      end
    end

    ```

*   **WMS Integration:**

    ```matlab
    %  (Example:  Simulating WMS task generation)

    function task_queue = initialize_task_queue_from_wms()
      % Replace with actual WMS API calls to retrieve tasks
      %  This is a placeholder for how you'd get tasks from a real WMS.

      % Example: Read tasks from a file (e.g., CSV)
      task_data = readtable('wms_task_data.csv'); % Requires a CSV file with columns: pickup_x, pickup_y, dropoff_x, dropoff_y, priority
      num_tasks = height(task_data);

      for i = 1:num_tasks
        task_queue(i).id = i;
        task_queue(i).pickup_location = [task_data.pickup_x(i), task_data.pickup_y(i)];
        task_queue(i).dropoff_location = [task_data.dropoff_x(i), task_data.dropoff_y(i)];
        task_queue(i).status = "Pending";
        task_queue(i).priority = task_data.priority(i);
      end
    end
    ```

**Summary**

Building a real-world AGV system is a complex undertaking. The MATLAB simulation provides a valuable starting point for design, testing, and optimization. However, careful consideration must be given to hardware selection, navigation technology, warehouse infrastructure, control system software, safety, and maintenance. The code examples above give you an idea of the transition required from a simplified simulation to a more realistic representation of AGV behavior. Remember to thoroughly test and validate your system before deploying it in a production environment.