​
What is Space Mission Planning?

Space Mission Planning is a complex process that involves analyzing mission parameters, calculating resource requirements, planning contingencies, optimizing resource allocation, and simulating various scenarios. This ensures the safety and success of space missions by carefully managing resources and preparing for potential emergencies.

​
Features

Mission Analysis

Analysis of mission duration, crew size, orbital parameters, and objectives.

Resource Calculation

Computation of life support, power, and propellant requirements.

Contingency Planning

Planning for power failures, life support malfunctions, and other emergencies.

Resource Optimization

Optimization of resource allocation with buffers for contingencies.

Scenario Simulation

Simulation of nominal operations and emergency scenarios.

​
Quick Start

1

Install Package

First, install the PraisonAI Agents package:
pip install praisonaiagents
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:
export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file app.py with the basic setup:
from praisonaiagents import Agent, Task, PraisonAIAgents
import time
from typing import Dict, List
import asyncio

def analyze_mission_parameters():
    """Simulates mission parameter analysis"""
    parameters = {
        "duration": 180,  # days
        "crew_size": 4,
        "orbital_parameters": {
            "altitude": 400,  # km
            "inclination": 51.6,  # degrees
            "period": 92.68  # minutes
        },
        "mission_objectives": [
            "scientific_research",
            "satellite_deployment",
            "space_station_maintenance"
        ]
    }
    return parameters

def calculate_resource_requirements(params: Dict):
    """Calculates required resources"""
    requirements = {
        "life_support": {
            "oxygen": params["duration"] * params["crew_size"] * 0.84,  # kg
            "water": params["duration"] * params["crew_size"] * 2.5,  # liters
            "food": params["duration"] * params["crew_size"] * 1.8   # kg
        },
        "power": {
            "daily_consumption": 30 + (params["crew_size"] * 5),  # kWh
            "peak_demand": 45 + (params["crew_size"] * 8),  # kW
            "backup_capacity": 72  # hours
        },
        "propellant": {
            "main_engine": 2000,  # kg
            "attitude_control": 500,  # kg
            "reserve": 300  # kg
        }
    }
    return requirements

def plan_contingencies(requirements: Dict):
    """Plans contingency scenarios"""
    contingencies = [
        {
            "scenario": "power_failure",
            "probability": 0.05,
            "impact": "critical",
            "resources_needed": {
                "backup_power": requirements["power"]["daily_consumption"] * 3,
                "repair_equipment": ["solar_panel_kit", "power_distribution_unit"]
            }
        },
        {
            "scenario": "life_support_malfunction",
            "probability": 0.03,
            "impact": "severe",
            "resources_needed": {
                "oxygen_reserve": requirements["life_support"]["oxygen"] * 0.2,
                "repair_parts": ["filter_system", "pressure_regulators"]
            }
        }
    ]
    return contingencies[int(time.time()) % 2]

def optimize_allocation(requirements: Dict, contingencies: Dict):
    """Optimizes resource allocation"""
    allocation = {
        "primary_resources": {
            "life_support": {
                "nominal": requirements["life_support"],
                "buffer": 0.15  # 15% buffer
            },
            "power": {
                "nominal": requirements["power"],
                "buffer": 0.2   # 20% buffer
            }
        },
        "contingency_resources": {
            "type": contingencies["scenario"],
            "allocation": contingencies["resources_needed"],
            "priority": "high" if contingencies["impact"] == "critical" else "medium"
        }
    }
    return allocation

def simulate_mission_scenarios(allocation: Dict):
    """Simulates various mission scenarios"""
    scenarios = {
        "nominal_operations": {
            "success_rate": 0.95,
            "resource_utilization": 0.85,
            "efficiency_rating": 0.9
        },
        "emergency_scenarios": [
            {
                "type": "power_reduction",
                "duration": 48,  # hours
                "impact": "moderate",
                "resolution_success": 0.88
            },
            {
                "type": "life_support_adjustment",
                "duration": 24,  # hours
                "impact": "minor",
                "resolution_success": 0.92
            }
        ]
    }
    return scenarios

# Create specialized agents
mission_analyzer = Agent(
    name="Mission Analyzer",
    role="Mission Analysis",
    goal="Analyze mission parameters",
    instructions="Evaluate mission requirements and constraints",
    tools=[analyze_mission_parameters]
)

resource_calculator = Agent(
    name="Resource Calculator",
    role="Resource Calculation",
    goal="Calculate resource requirements",
    instructions="Determine required resources for mission",
    tools=[calculate_resource_requirements]
)

contingency_planner = Agent(
    name="Contingency Planner",
    role="Contingency Planning",
    goal="Plan for contingencies",
    instructions="Develop contingency scenarios and plans",
    tools=[plan_contingencies]
)

resource_optimizer = Agent(
    name="Resource Optimizer",
    role="Resource Optimization",
    goal="Optimize resource allocation",
    instructions="Optimize resource distribution",
    tools=[optimize_allocation]
)

scenario_simulator = Agent(
    name="Scenario Simulator",
    role="Scenario Simulation",
    goal="Simulate mission scenarios",
    instructions="Simulate various mission scenarios",
    tools=[simulate_mission_scenarios]
)

# Create workflow tasks
mission_task = Task(
    name="analyze_mission",
    description="Analyze mission parameters",
    expected_output="Mission parameters",
    agent=mission_analyzer,
    is_start=True,
    next_tasks=["calculate_resources"]
)

resource_task = Task(
    name="calculate_resources",
    description="Calculate resource requirements",
    expected_output="Resource requirements",
    agent=resource_calculator,
    next_tasks=["plan_contingencies"]
)

contingency_task = Task(
    name="plan_contingencies",
    description="Plan contingencies",
    expected_output="Contingency plans",
    agent=contingency_planner,
    context=[resource_task],
    next_tasks=["optimize_resources"]
)

optimization_task = Task(
    name="optimize_resources",
    description="Optimize resource allocation",
    expected_output="Resource allocation",
    agent=resource_optimizer,
    context=[resource_task, contingency_task],
    next_tasks=["simulate_scenarios"]
)

simulation_task = Task(
    name="simulate_scenarios",
    description="Simulate scenarios",
    expected_output="Simulation results",
    agent=scenario_simulator,
    task_type="decision",
    condition={
        "nominal_operations": "",  # End workflow if nominal
        "emergency_scenarios": ["optimize_resources"]  # Reoptimize if emergency
    }
)

# Create workflow
workflow = PraisonAIAgents(
    agents=[mission_analyzer, resource_calculator, contingency_planner,
            resource_optimizer, scenario_simulator],
    tasks=[mission_task, resource_task, contingency_task,
           optimization_task, simulation_task],
    process="workflow",
    verbose=True
)

async def main():
    print("\nStarting Space Mission Resource Optimization Workflow...")
    print("=" * 50)
    
    # Run workflow
    results = await workflow.astart()
    
    # Print results
    print("\nResource Optimization Results:")
    print("=" * 50)
    for task_id, result in results["task_results"].items():
        if result:
            print(f"\nTask: {task_id}")
            print(f"Result: {result.raw}")
            print("-" * 50)

if __name__ == "__main__":
    asyncio.run(main())

​
Next Steps

Introduction

Learn more about PraisonAI and its core concepts

Quick Start

Get started with the basics of PraisonAI