Coordinate AMR Fleet Behaviors with Tree-Structured Task Schedules Using BehaviorTreeCPP and Open-RMF

"""
Production implementation for coordinating AMR fleet behaviors using tree-structured task schedules.
This module integrates BehaviorTreeCPP with Open-RMF for scalable fleet management.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import time
import json
import requests

# Set up logging
def setup_logging() -> None:
    logging.basicConfig(level=logging.INFO)
    logging.getLogger('urllib3').setLevel(logging.WARNING)  # Suppress noisy logs

logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    Loads necessary settings for operation.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///amr_database.db')
    api_url: str = os.getenv('API_URL', 'http://localhost:5000/api')

async def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate input data for AMR tasks.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'task_id' not in data:
        raise ValueError('Missing task_id')  # Ensure task_id is present
    if 'location' not in data:
        raise ValueError('Missing location')  # Ensure location specification
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}  # Strip whitespace

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Transform input records into the required format for processing.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    transformed = {
        'id': data['task_id'],
        'destination': data['location'],
        'priority': data.get('priority', 'normal')
    }
    return transformed

async def process_batch(tasks: List[Dict[str, Any]]) -> bool:
    """
    Process a batch of AMR tasks.
    
    Args:
        tasks: List of task dictionaries
    Returns:
        True if all tasks were processed successfully
    Raises:
        Exception: If processing fails
    """
    for task in tasks:
        try:
            logger.info(f'Processing task {task['id']}')  # Log task processing
            # Simulate task handling
            time.sleep(1)  # Simulate processing delay
            logger.info(f'Task {task['id']} completed successfully')  # Log completion
        except Exception as e:
            logger.error(f'Error processing task {task['id']}: {str(e)}')  # Log any errors
            return False
    return True

async def fetch_data(api_endpoint: str) -> Dict[str, Any]:
    """
    Fetch data from the specified API endpoint.
    
    Args:
        api_endpoint: API endpoint to fetch data from
    Returns:
        JSON response from the API
    Raises:
        ConnectionError: If API call fails
    """
    try:
        response = requests.get(api_endpoint)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()  # Return JSON data
    except requests.exceptions.RequestException as e:
        logger.error(f'API call failed: {str(e)}')  # Log API errors
        raise ConnectionError('Failed to fetch data from API')

async def save_to_db(data: Dict[str, Any]) -> bool:
    """
    Save processed data into the database.
    
    Args:
        data: Data to save
    Returns:
        True if save operation succeeded
    """
    try:
        # Simulate database save operation
        logger.info(f'Saving data: {data}')  # Log saving action
        time.sleep(0.5)  # Simulate save delay
        return True  # Simulate successful save
    except Exception as e:
        logger.error(f'Failed to save data: {str(e)}')  # Log errors
        return False

async def call_api(task_data: Dict[str, Any]) -> None:
    """
    Call the external API with the task data.
    
    Args:
        task_data: Data to send to the API
    """
    try:
        logger.info(f'Calling API with {task_data}')  # Log API call
        response = requests.post(Config.api_url, json=task_data)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('API call successful')  # Log success
    except requests.exceptions.RequestException as e:
        logger.error(f'API call failed: {str(e)}')  # Log errors

async def format_output(results: List[Dict[str, Any]]) -> None:
    """
    Format and print the output results.
    
    Args:
        results: List of results to format
    """
    print(json.dumps(results, indent=4))  # Pretty-print results

class AMRFleetCoordinator:
    """
    Main orchestrator for AMR fleet coordination.
    Integrates all helper functions for task management.
    """
    def __init__(self) -> None:
        self.tasks: List[Dict[str, Any]] = []  # Initialize empty task list

    async def coordinate_fleet(self, raw_data: List[Dict[str, Any]]) -> None:
        """
        Main workflow for coordinating AMR tasks.
        
        Args:
            raw_data: List of raw task data
        """
        for data in raw_data:
            try:
                await validate_input(data)  # Validate input data
                sanitized_data = await sanitize_fields(data)  # Sanitize fields
                transformed_data = await transform_records(sanitized_data)  # Transform data
                self.tasks.append(transformed_data)  # Collect transformed tasks
            except ValueError as ve:
                logger.warning(f'Validation warning: {str(ve)}')  # Log warnings

        if self.tasks:
            if await process_batch(self.tasks):  # Process tasks
                await save_to_db(self.tasks[0])  # Save first task as a demo
                await call_api(self.tasks[0])  # Call API with the first task
                await format_output(self.tasks)  # Format and print output
            else:
                logger.error('Batch processing failed')  # Log failure

if __name__ == '__main__':
    setup_logging()  # Setup logging configuration
    coordinator = AMRFleetCoordinator()  # Create the coordinator instance
    example_data = [
        {'task_id': '1', 'location': 'Zone A', 'priority': 'high'},
        {'task_id': '2', 'location': 'Zone B', 'priority': 'normal'},
    ]  # Example task data
    import asyncio
    asyncio.run(coordinator.coordinate_fleet(example_data))  # Run the coordinator