# Define the Robot Type
mutable struct Robot
    id::Int
    position::Tuple{Float64, Float64}
    velocity::Tuple{Float64, Float64}
    # Add additional robot attributes here
end

# Define the Environment Type
mutable struct Environment
    obstacles::Vector{Tuple{Float64, Float64}}
    # Add additional environment attributes here
end

# Define the Communication Protocol
abstract type Message end

struct CommandMessage <: Message
    target_robot::Int
    command::Symbol
    # Add additional message fields here
end

struct DataMessage <: Message
    sender::Int
    data::Any
    # Add additional message fields here
end

# Define the Swarm Type
mutable struct Swarm
    robots::Vector{Robot}
    environment::Environment
    communication_network::Dict{Int, Vector{Message}}
    # Add additional swarm attributes here
end

# Define Decentralized Decision-Making Algorithm
function decentralized_decision_making(robot::Robot, swarm::Swarm)
    # Implement decision-making logic here
    # Example: Reinforcement learning-based approach
    # Placeholder code:
    robot.velocity = (randn(), randn())  # Random velocity for demonstration
end

# Define Swarm Coordination and Task Allocation Algorithm
function swarm_coordination_task_allocation(swarm::Swarm)
    # Implement task allocation logic here
    # Example: Genetic algorithm-based approach
    # Placeholder code:
    for robot in swarm.robots
        robot.position = (rand(), rand())  # Random position for demonstration
    end
end

# Define Adaptive Navigation and Obstacle Avoidance Algorithm
function adaptive_navigation_obstacle_avoidance(robot::Robot, swarm::Swarm)
    # Implement navigation and obstacle avoidance logic here
    # Example: Potential field-based approach
    # Placeholder code:
    for obstacle in swarm.environment.obstacles
        # Compute repulsive force from obstacles
        repulsive_force_x = 1 / ((robot.position[1] - obstacle[1])^2 + 1e-6)  # Avoid division by zero
        repulsive_force_y = 1 / ((robot.position[2] - obstacle[2])^2 + 1e-6)  # Avoid division by zero
        # Update robot velocity based on repulsive forces
        robot.velocity = (robot.velocity[1] + repulsive_force_x, robot.velocity[2] + repulsive_force_y)
    end
end

# Define Swarm Dynamics and Emergent Behavior Simulation
function simulate_swarm_dynamics(swarm::Swarm)
    # Implement simulation of swarm dynamics and emergent behavior here
    # Example: Agent-based simulation with collision detection
    # Placeholder code:
    for i in 1:length(swarm.robots)
        for j in (i+1):length(swarm.robots)
            if norm(swarm.robots[i].position - swarm.robots[j].position) < 0.1  # Adjust collision threshold as needed
                # Handle collision between robots i and j
                # Example: Randomly adjust positions to resolve collision
                swarm.robots[i].position = (rand(), rand())
                swarm.robots[j].position = (rand(), rand())
            end
        end
    end
end

# Define Integration with Robot Hardware
function integrate_with_robot_hardware(robot::Robot)
    # Implement integration with robot hardware here
    # Example: Control robot actuators and read sensor data
    # Placeholder code:
    robot.position = (robot.position[1] + robot.velocity[1], robot.position[2] + robot.velocity[2])  # Update robot position based on velocity
end

# Define Validation and Testing Procedures
function validate_and_test_framework(swarm::Swarm)
    # Implement validation and testing procedures here
    # Example: Simulation studies, hardware-in-the-loop experiments, field trials
    # Placeholder code:
    for robot in swarm.robots
        println("Robot ID: ", robot.id)
        println("Position: ", robot.position)
        println("Velocity: ", robot.velocity)
    end
end

# Define Communication Handling
function handle_communication(swarm::Swarm)
    for robot in swarm.robots
        if haskey(swarm.communication_network, robot.id)
            messages = swarm.communication_network[robot.id]
            for message in messages
                if isa(message, CommandMessage)
                    # Process command message
                    # Example: Execute command on the robot
                    println("Received command: ", message.command, " for robot ", message.target_robot)
                elseif isa(message, DataMessage)
                    # Process data message
                    # Example: Update robot's internal state based on received data
                    println("Received data from robot ", message.sender, ": ", message.data)
                end
            end
            swarm.communication_network[robot.id] = []  # Clear received messages
        end
    end
end

# Define Communication Simulation
function simulate_communication(swarm::Swarm)
    # Simulate message passing in the communication network
    for robot in swarm.robots
        # Simulate sending messages
        for neighbor_robot in swarm.robots
            if robot.id != neighbor_robot.id
                # Simulate sending command message
                push!(swarm.communication_network[neighbor_robot.id], CommandMessage(robot.id, :move))
                # Simulate sending data message
                push!(swarm.communication_network[neighbor_robot.id], DataMessage(robot.id, (rand(), rand())))
            end
        end
    end
end

# Main Function
function main()
    # Initialize swarm of robots
    num_robots = 10
    robots = [Robot(i, (rand(), rand()), (rand(), rand())) for i in 1:num_robots]
    obstacles = [(rand(), rand()) for _ in 1:5]  # Generate random obstacles
    environment = Environment(obstacles)
    communication_network = Dict{Int, Vector{Message}}()
    for robot in robots
        communication_network[robot.id] = []
    end
    swarm = Swarm(robots, environment, communication_network)
    
    # Start swarm coordination loop
    while true
        # Simulate communication
        simulate_communication(swarm)
        
        # Handle received messages
        handle_communication(swarm)
        
        # Execute decentralized decision-making for each robot
        for robot in swarm.robots
            decentralized_decision_making(robot, swarm)
        end
        
        # Perform swarm coordination and task allocation
        swarm_coordination_task_allocation(swarm)
        
        # Execute adaptive navigation and obstacle avoidance for each robot
        for robot in swarm.robots
            adaptive_navigation_obstacle_avoidance(robot, swarm)
        end
        
        # Simulate swarm dynamics and emergent behavior
        simulate_swarm_dynamics(swarm)
        
        # Integrate with robot hardware
        for robot in swarm.robots
            integrate_with_robot_hardware(robot)
        end
        
        # Validate and test framework
        validate_and_test_framework(swarm)
        
        # Add additional logic and termination conditions as needed
    end
end

# Entry Point
main()

