├── .gitignore ├── docs ├── HabitatHub.jpeg ├── installation.md ├── user_manual.md ├── architecture.md └── design.md ├── .github └── ISSUE_TEMPLATE │ ├── custom.md │ ├── feature_request.md │ └── bug_report.md ├── README.md ├── deployment ├── habitat_deployment │ └── habitat_deployment.py ├── habitat_monitoring │ └── habitat_monitoring.py └── habitat_maintenance │ └── habitat_maintenance.py ├── tools ├── habitat_simulator │ └── habitat_simulator.py ├── habitat_analyzer │ └── habitat_analyzer.py └── habitat_designer │ └── habitat_designer.py ├── src ├── habitat │ ├── utils │ │ └── utils.py │ ├── modules │ │ ├── food_production │ │ │ └── food_production.py │ │ ├── energy_generation │ │ │ └── energy_generation.py │ │ ├── robotics │ │ │ └── robotics.py │ │ └── life_support │ │ │ └── life_support.py │ └── systems │ │ └── systems.py ├── data │ └── data_management.py ├── tests │ └── test.py └── autonomous │ └── autonomous_systems.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | # Google App Engine generated folder 2 | appengine-generated/ 3 | -------------------------------------------------------------------------------- /docs/HabitatHub.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KOSASIH/HabitatHub/HEAD/docs/HabitatHub.jpeg -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/custom.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Custom issue template 3 | about: Describe this issue template's purpose here. 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | 11 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /docs/installation.md: -------------------------------------------------------------------------------- 1 | # HabitatHub Installation 2 | 3 | This guide provides step-by-step instructions for installing HabitatHub on your system. 4 | 5 | ## Prerequisites 6 | 7 | * Python 3.x 8 | * C++ compiler (for performance-critical components) 9 | * MySQL (for database management) 10 | * Matplotlib and Seaborn (for data visualization) 11 | * Pandas and NumPy (for data analysis) 12 | 13 | ## Installation Steps 14 | 15 | 1. Clone the HabitatHub repository: `git clone https://github.com/KOSASIH/HabitatHub.git` 16 | 2. Install the required dependencies: `pip install -r requirements.txt` 17 | 3. Configure the database: `mysql -u root -p < habitat_hub.sql` 18 | 4. Build the C++ components: `cmake . && make` 19 | 5. Install the HabitatHub package: `pip install .` 20 | 21 | ## Running HabitatHub 22 | 23 | 1. Start the HabitatHub server: `python habitat_hub_server.py` 24 | 2. Access the HabitatHub web interface: `http://localhost:8000` 25 | 26 | This installation guide provides a basic setup for HabitatHub. For more advanced configurations and customization, please refer to the user manual. 27 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # HabitatHub 2 | Modular, autonomous space habitats for sustainable living. 3 | 4 | # HabitatHub 5 | 6 | HabitatHub is a modular, autonomous space habitat project designed to support human life in space. 7 | 8 | # Description 9 | 10 | HabitatHub is a cutting-edge space habitat designed to create a sustainable, self-sufficient habitat that can be easily assembled and maintained in space. The habitat is autonomous, meaning it can operate independently without human intervention. 11 | 12 | # Features and Functionalities 13 | 14 | 1. Modular design for easy assembly and maintenance 15 | 2. Autonomous operation with minimal human intervention 16 | 3. Includes life support, power generation, communication, and agent modules 17 | 3. Supports real-time monitoring and maintenance 18 | 19 | # Getting Started 20 | 21 | To get started with HabitatHub, follow these steps: 22 | 23 | 1. Clone the repository to your local machine 24 | 2. Install the required dependencies and libraries 25 | 3. Follow the instructions in the deployment script to deploy the habitat in space 26 | 27 | # Contributing 28 | 29 | Contributions to HabitatHub are welcome! If you're interested in contributing, please fork the repository and submit a pull request. 30 | 31 | # License and Copyright 32 | 33 | HabitatHub is licensed under the Apache 2.0 License. See the LICENSE file for details. 34 | 35 | Acknowledgments 36 | HabitatHub would not be possible without the contributions of the following individuals and organizations: 37 | 38 | - All contributors 39 | 40 | -------------------------------------------------------------------------------- /docs/user_manual.md: -------------------------------------------------------------------------------- 1 | # HabitatHub User Manual 2 | 3 | This user manual provides a comprehensive guide to using HabitatHub, including its features, tools, and interfaces. 4 | 5 | ## HabitatHub Web Interface 6 | 7 | The HabitatHub web interface provides a user-friendly interface for monitoring and controlling the habitat . The interface is divided into several sections: 8 | 9 | * **Dashboard**: Displays an overview of the habitat's systems and performance metrics. 10 | * **Habitat Modules**: Allows users to monitor and control the habitat's modules, including life support, energy generation, food production, and robotics. 11 | * **Autonomous Systems**: Provides access to the habitat's autonomous systems, including decision-making, navigation, and control. 12 | * **Data Management**: Offers tools for data visualization, analysis, and management. 13 | * **Tools and Interfaces**: Provides access to the habitat designer, simulator, and analyzer tools. 14 | 15 | ## Habitat Designer Tool 16 | 17 | The Habitat Designer Tool allows users to design and configure the habitat's modules and systems. The tool provides a graphical user interface (GUI) and computer-aided design (CAD) capabilities. 18 | 19 | ## Habitat Simulator Tool 20 | 21 | The Habitat Simulator Tool enables users to simulate scenarios and test the habitat's systems in a virtual environment. 22 | 23 | ## Habitat Analyzer Tool 24 | 25 | The Habitat Analyzer Tool provides performance metrics, resource utilization, and system health monitoring for the habitat. 26 | 27 | This user manual provides a comprehensive guide to using HabitatHub. For more information, please refer to the architecture and design documents. 28 | -------------------------------------------------------------------------------- /docs/architecture.md: -------------------------------------------------------------------------------- 1 | # HabitatHub Architecture 2 | 3 | HabitatHub is a modular, autonomous space habitat designed for sustainable living in space. The architecture is divided into several layers, each responsible for a specific aspect of the habitat's functionality. 4 | 5 | ## Layers 6 | 7 | ### 1. Habitat Modules 8 | 9 | * Life Support System (LSS) 10 | * Energy Generation System (EGS) 11 | * Food Production System (FPS) 12 | * Robotics and Automation System (RAS) 13 | 14 | ### 2. Autonomous Systems 15 | 16 | * Decision-Making System (DMS) 17 | * Navigation System (NS) 18 | * Control System (CS) 19 | 20 | ### 3. Data Management 21 | 22 | * Database Management System (DBMS) 23 | * Data Visualization System (DVS) 24 | * Data Analysis System (DAS) 25 | 26 | ### 4. Tools and Interfaces 27 | 28 | * Habitat Designer Tool (HDT) 29 | * Habitat Simulator Tool (HST) 30 | * Habitat Analyzer Tool (HAT) 31 | 32 | ### 5. Deployment and Maintenance 33 | 34 | * Habitat Deployment Script (HDS) 35 | * Habitat Monitoring Script (HMS) 36 | * Habitat Maintenance Script (HMS) 37 | 38 | ## Communication Flow 39 | 40 | The communication flow between the layers is as follows: 41 | 42 | * Habitat Modules → Autonomous Systems → Data Management → Tools and Interfaces 43 | * Autonomous Systems → Habitat Modules 44 | * Data Management → Autonomous Systems 45 | * Tools and Interfaces → Data Management 46 | 47 | ## Technology Stack 48 | 49 | * Python 3.x for the majority of the codebase 50 | * C++ for performance-critical components 51 | * MySQL for database management 52 | * Matplotlib and Seaborn for data visualization 53 | * Pandas and NumPy for data analysis 54 | 55 | This architecture provides a modular and scalable design for the HabitatHub system, allowing for easy integration of new components and technologies as needed. 56 | -------------------------------------------------------------------------------- /deployment/habitat_deployment/habitat_deployment.py: -------------------------------------------------------------------------------- 1 | # habitat_deployment.py 2 | 3 | import habitat 4 | 5 | class HabitatDeployer: 6 | def __ init__(self, habitat_design): 7 | self.habitat_design = habitat_design 8 | self.deployment = {} 9 | 10 | def deploy_habitat(self): 11 | # Deploy the habitat based on the design 12 | self.deployment['modules'] = self.deploy_modules() 13 | self.deployment['systems'] = self.deploy_systems() 14 | self.deployment['agents'] = self.deploy_agents() 15 | 16 | return self.deployment 17 | 18 | def deploy_modules(self): 19 | # Deploy the modules of the habitat 20 | modules = [] 21 | for module in self.habitat_design['modules']: 22 | module_instance = habitat.Module(module['name'], module['type'], module['dimensions']) 23 | modules.append(module_instance) 24 | return modules 25 | 26 | def deploy_systems(self): 27 | # Deploy the systems of the habitat 28 | systems = { 29 | 'life_support': habitat.LifeSupportSystem(), 30 | 'power': habitat.PowerSystem(), 31 | 'communication': habitat.CommunicationSystem() 32 | } 33 | return systems 34 | 35 | def deploy_agents(self): 36 | # Deploy the agents in the habitat 37 | agents = [] 38 | for i in range(5): 39 | agent = habitat.Agent(f'Agent {i+1}', 'astronaut', (np.random.uniform(0, 100), np.random.uniform(0, 100), np.random.uniform(0, 100))) 40 | agents.append(agent) 41 | return agents 42 | 43 | def main(): 44 | habitat_designer = HabitatDesigner('space_station', (100, 100, 100)) 45 | habitat_design = habitat_designer.design_habitat() 46 | habitat_deployer = HabitatDeployer(habitat_design) 47 | deployment = habitat_deployer.deploy_habitat() 48 | print(deployment) 49 | 50 | if __name__ == '__main__': 51 | main() 52 | -------------------------------------------------------------------------------- /docs/design.md: -------------------------------------------------------------------------------- 1 | # HabitatHub Design 2 | 3 | The HabitatHub design is focused on creating a sustainable, modular, and autonomous space habitat. The design is divided into several sections, each addressing a specific aspect of the habitat's functionality. 4 | 5 | ## Habitat Modules 6 | 7 | ### Life Support System (LSS) 8 | 9 | * Air Quality Control 10 | * Water Purification 11 | * Waste Management 12 | * Temperature and Humidity Control 13 | 14 | ### Energy Generation System (EGS) 15 | 16 | * Solar Panels 17 | * Fuel Cells 18 | * Nuclear Reactors 19 | 20 | ### Food Production System (FPS) 21 | 22 | * Hydroponics 23 | * Aeroponics 24 | * Algae Farming 25 | 26 | ### Robotics and Automation System (RAS) 27 | 28 | * Maintenance and Repair 29 | * Upgrade and Replacement 30 | * Monitoring and Inspection 31 | 32 | ## Autonomous Systems 33 | 34 | ### Decision-Making System (DMS) 35 | 36 | * Expert Systems 37 | * Fuzzy Logic 38 | * Neural Networks 39 | 40 | ### Navigation System (NS) 41 | 42 | * Trajectory Planning 43 | * Obstacle Avoidance 44 | * Docking and Berthing 45 | 46 | ### Control System (CS) 47 | 48 | * Attitude Control 49 | * Temperature Control 50 | * Humidity Control 51 | 52 | ## Data Management 53 | 54 | ### Database Management System (DBMS) 55 | 56 | * MySQL 57 | * PostgreSQL 58 | * MongoDB 59 | 60 | ### Data Visualization System (DVS) 61 | 62 | * Matplotlib 63 | * Seaborn 64 | * Plotly 65 | 66 | ### Data Analysis System (DAS) 67 | 68 | * Pandas 69 | * NumPy 70 | * SciPy 71 | 72 | ## Tools and Interfaces 73 | 74 | ### Habitat Designer Tool (HDT) 75 | 76 | * Graphical User Interface (GUI) 77 | * Computer-Aided Design (CAD) 78 | 79 | ### Habitat Simulator Tool (HST) 80 | 81 | * Simulation Environment 82 | * Scenario-Based Testing 83 | 84 | ### Habitat Analyzer Tool (HAT) 85 | 86 | * Performance Metrics 87 | * Resource Utilization 88 | * System Health Monitoring 89 | 90 | This design provides a comprehensive and integrated approach to creating a sustainable and autonomous space habitat. 91 | -------------------------------------------------------------------------------- /tools/habitat_simulator/habitat_simulator.py: -------------------------------------------------------------------------------- 1 | # habitat_simulator.py 2 | 3 | import numpy as np 4 | 5 | class HabitatSimulator: 6 | def __init__(self, habitat_design): 7 | self.habitat_design = habitat_design 8 | self.simulation = {} 9 | 10 | def simulate_habitat(self): 11 | # Simulate the habitat based on the design 12 | self.simulation['environment'] = self.simulate_environment() 13 | self.simulation['systems'] = self.simulate_systems() 14 | self.simulation['agents'] = self.simulate_agents() 15 | 16 | return self.simulation 17 | 18 | def simulate_environment(self): 19 | # Simulate the environment of the habitat 20 | environment = { 21 | 'temperature': np.random.uniform(20, 30), 22 | 'humidity': np.random.uniform(50, 70), 23 | 'atmosphere': np.random.uniform(100, 110) 24 | } 25 | return environment 26 | 27 | def simulate_systems(self): 28 | # Simulate the systems of the habitat 29 | systems = { 30 | 'life_support': {'oxygen': 90, 'water': 80, 'food': 70}, 31 | 'power': {'solar_panels': 8, 'batteries': 9}, 32 | 'communication': {'antennas': 4, 'transceivers': 5} 33 | } 34 | return systems 35 | 36 | def simulate_agents(self): 37 | # Simulate the agents in the habitat 38 | agents = [] 39 | for i in range(5): 40 | agent = { 41 | 'name': f'Agent {i+1}', 42 | 'type': 'astronaut', 43 | 'location': (np.random.uniform(0, 100), np.random.uniform(0, 100), np.random.uniform(0, 100)) 44 | } 45 | agents.append(agent) 46 | return agents 47 | 48 | def main(): 49 | habitat_designer = HabitatDesigner('space_station', (100, 100, 100)) 50 | habitat_design = habitat_designer.design_habitat() 51 | habitat_simulator = HabitatSimulator(habitat_design) 52 | simulation = habitat_simulator.simulate_habitat() 53 | print(simulation) 54 | 55 | if __name__ == '__main__': 56 | main() 57 | -------------------------------------------------------------------------------- /deployment/habitat_monitoring/habitat_monitoring.py: -------------------------------------------------------------------------------- 1 | # habitat_monitoring.py 2 | 3 | import habitat 4 | 5 | class HabitatMonitor: 6 | def __init__(self, habitat_deployment): 7 | self.habitat_deployment = habitat_deployment 8 | self.monitoring = {} 9 | 10 | def monitor_habitat(self): 11 | # Monitor the habitat based on the deployment 12 | self.monitoring['environment'] = self.monitor_environment() 13 | self.monitoring['systems'] = self.monitor_systems() 14 | self.monitoring['agents'] = self.monitor_agents() 15 | 16 | return self.monitoring 17 | 18 | def monitor_environment(self): 19 | # Monitor the environment of the habitat 20 | environment = { 21 | 'temperature': np.random.uniform(20, 30), 22 | 'humidity': np.random.uniform(50, 70), 23 | 'atmosphere': np.random.uniform(100, 110) 24 | } 25 | return environment 26 | 27 | def monitor_systems(self): 28 | # Monitor the systems of the habitat 29 | systems = { 30 | 'life_support': {'oxygen': 90, 'water': 80, 'food': 70}, 31 | 'power': {'solar_panels': 8, 'batteries': 9}, 32 | 'communication': {'antennas': 4, 'transceivers': 5} 33 | } 34 | return systems 35 | 36 | def monitor_agents(self): 37 | # Monitor the agents in the habitat 38 | agents = [] 39 | for agent in self.habitat_deployment['agents']: 40 | agent_status = { 41 | 'name': agent.name, 42 | 'location': agent.location, 43 | 'status': np.random.choice(['active', 'inactive']) 44 | } 45 | agents.append(agent_status) 46 | return agents 47 | 48 | def main(): 49 | habitat_designer = HabitatDesigner('space_station', (100, 100, 100)) 50 | habitat_design = habitat_designer.design_habitat() 51 | habitat_deployer = HabitatDeployer(habitat_design) 52 | habitat_deployment = habitat_deployer.deploy_habitat() 53 | habitat_monitor = HabitatMonitor(habitat_deployment) 54 | monitoring = habitat_monitor.monitor_habitat() 55 | print(monitoring) 56 | 57 | if __name__ == '__main__': 58 | main() 59 | -------------------------------------------------------------------------------- /tools/habitat_analyzer/habitat_analyzer.py: -------------------------------------------------------------------------------- 1 | # habitat_analyzer.py 2 | 3 | import numpy as np 4 | 5 | class HabitatAnalyzer: 6 | def __init__(self, habitat_simulation): 7 | self.habitat_simulation = habitat_simulation 8 | self.analysis = {} 9 | 10 | def analyze_habitat(self): 11 | # Analyze the habitat based on the simulation 12 | self.analysis['environment'] = self.analyze_environment() 13 | self.analysis['systems'] = self.analyze_systems() 14 | self.analysis['agents'] = self.analyze_agents() 15 | 16 | return self.analysis 17 | 18 | def analyze_environment(self): 19 | # Analyze the environment of the habitat 20 | environment = self.habitat_simulation['environment'] 21 | analysis = { 22 | 'temperature': np.mean(environment['temperature']), 23 | 'humidity': np.mean(environment['humidity']), 24 | 'atmosphere': np.mean(environment['atmosphere']) 25 | } 26 | return analysis 27 | 28 | def analyze_systems(self): 29 | # Analyze the systems of the habitat 30 | systems = self.habitat_simulation['systems'] 31 | analysis = { 32 | 'life_support': np.mean(list(systems['life_support'].values())), 33 | 'power': np.mean(list(systems['power'].values())), 34 | 'communication': np.mean(list(systems['communication'].values())) 35 | } 36 | return analysis 37 | 38 | def analyze_agents(self): 39 | # Analyze the agents in the habitat 40 | agents = self.habitat_simulation['agents'] 41 | analysis = { 42 | 'agent_count': len(agents), 43 | 'agent_locations': [agent['location'] for agent in agents] 44 | } 45 | return analysis 46 | 47 | def main(): 48 | habitat_designer = HabitatDesigner('space_station', (100, 100, 100)) 49 | habitat_design = habitat_designer.design_habitat() 50 | habitat_simulator = HabitatSimulator(habitat_design) 51 | habitat_simulation = habitat_simulator.simulate_habitat() 52 | habitat_analyzer = HabitatAnalyzer(habitat_simulation) 53 | analysis = habitat_analyzer.analyze_habitat() 54 | print(analysis) 55 | 56 | if __name__ == '__main__': 57 | main() 58 | -------------------------------------------------------------------------------- /deployment/habitat_maintenance/habitat_maintenance.py: -------------------------------------------------------------------------------- 1 | # habitat_maintenance.py 2 | 3 | import habitat 4 | 5 | class HabitatMaintainer: 6 | def __init__(self, habitat_deployment): 7 | self.habitat_deployment = habitat_deployment 8 | self.maintenance = {} 9 | 10 | def maintain_habitat(self): 11 | # Maintain the habitat based on the deployment 12 | self.maintenance['systems'] = self.maintain_systems() 13 | self.maintenance['agents'] = self.maintain_agents() 14 | 15 | return self.maintenance 16 | 17 | def maintain_systems(self): 18 | # Maintain the systems of the habitat 19 | systems = { 20 | 'life_support': self.maintain_life_support(), 21 | 'power': self.maintain_power(), 22 | 'communication': self.maintain_communication() 23 | } 24 | return systems 25 | 26 | def maintain_life_support(self): 27 | # Maintain the life support system 28 | life_support = { 29 | 'oxygen': 100, 30 | 'water': 100, 31 | 'food': 100 32 | } 33 | return life_support 34 | 35 | def maintain_power(self): 36 | # Maintain the power system 37 | power = { 38 | 'solar_panels': 10, 39 | 'batteries': 10 40 | } 41 | return power 42 | 43 | def maintain_communication(self): 44 | # Maintain the communication system 45 | communication = { 46 | 'antennas': 5, 47 | 'transceivers': 5 48 | } 49 | return communication 50 | 51 | def maintain_agents(self): 52 | # Maintain the agents in the habitat 53 | agents = [] 54 | for agent in self.habitat_deployment['agents']: 55 | agent_status = { 56 | 'name': agent.name, 57 | 'location': agent.location, 58 | 'status': 'active' 59 | } 60 | agents.append(agent_status) 61 | return agents 62 | 63 | def main(): 64 | habitat_designer = HabitatDesigner('space_station', (100, 100, 100)) 65 | habitat_design = habitat_designer.design_habitat() 66 | habitat_deployer = HabitatDeployer(habitat_design) 67 | habitat_deployment = habitat_deployer.deploy_habitat() 68 | habitat_maintainer = HabitatMaintainer(habitat_deployment) 69 | maintenance = habitat_maintainer.maintain_habitat() 70 | print(maintenance) 71 | 72 | if __name__ == '__main__': 73 | main() 74 | -------------------------------------------------------------------------------- /tools/habitat_designer/habitat_designer.py: -------------------------------------------------------------------------------- 1 | # habitat_designer.py 2 | 3 | import numpy as np 4 | 5 | class HabitatDesigner: 6 | def __init__(self, habitat_type, dimensions): 7 | self.habitat_type = habitat_type 8 | self.dimensions = dimensions 9 | self.design = {} 10 | 11 | def design_habitat(self): 12 | # Design the habitat based on the type and dimensions 13 | if self.habitat_type == 'space_station': 14 | self.design['modules'] = self.design_space_station_modules() 15 | elif self.habitat_type == 'lunar_base': 16 | self.design['modules'] = self.design_lunar_base_modules() 17 | else: 18 | raise ValueError('Invalid habitat type') 19 | 20 | self.design['layout'] = self.design_habitat_layout() 21 | self.design['systems'] = self.design_habitat_systems() 22 | 23 | return self.design 24 | 25 | def design_space_station_modules(self): 26 | # Design the modules for a space station 27 | modules = [] 28 | for i in range(5): 29 | module = { 30 | 'name': f'Module {i+1}', 31 | 'type': 'living_quarters', 32 | 'dimensions': (10, 10, 10) 33 | } 34 | modules.append(module) 35 | return modules 36 | 37 | def design_lunar_base_modules(self): 38 | # Design the modules for a lunar base 39 | modules = [] 40 | for i in range(3): 41 | module = { 42 | 'name': f'Module {i+1}', 43 | 'type': 'research_lab', 44 | 'dimensions': (20, 20, 20) 45 | } 46 | modules.append(module) 47 | return modules 48 | 49 | def design_habitat_layout(self): 50 | # Design the layout of the habitat 51 | layout = np.zeros((self.dimensions[0], self.dimensions[1], self.dimensions[2])) 52 | for module in self.design['modules']: 53 | x, y, z = module['dimensions'] 54 | layout[x:x+10, y:y+10, z:z+10] = 1 55 | return layout 56 | 57 | def design_habitat_systems(self): 58 | # Design the systems for the habitat 59 | systems = { 60 | 'life_support': {'oxygen': 100, 'water': 100, 'food': 100}, 61 | 'power': {'solar_panels': 10, 'batteries': 10}, 62 | 'communication': {'antennas': 5, 'transceivers': 5} 63 | } 64 | return systems 65 | 66 | def main(): 67 | habitat_designer = HabitatDesigner('space_station', (100, 100, 100)) 68 | design = habitat_designer.design_habitat() 69 | print(design) 70 | 71 | if __name__ == '__main__': 72 | main() 73 | -------------------------------------------------------------------------------- /src/habitat/utils/utils.py: -------------------------------------------------------------------------------- 1 | # utils.py 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.ensemble import RandomForestRegressor 6 | from sklearn.metrics import mean_squared_error 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | import rospy 11 | import roslib 12 | import tf 13 | 14 | class DataAnalysis: 15 | def __init__(self): 16 | self.data = [] 17 | 18 | def load_data(self, file_path): 19 | # Load data from CSV file 20 | self.data = pd.read_csv(file_path) 21 | 22 | def clean_data(self): 23 | # Clean data by removing missing values and outliers 24 | self.data.dropna(inplace=True) 25 | self.data = self.data[(np.abs(self.data) < 3 * np.std(self.data)).all(axis=1)] 26 | 27 | def visualize_data(self): 28 | # Visualize data using matplotlib 29 | plot(self.data) 30 | show() 31 | 32 | class MachineLearning: 33 | def __init__(self): 34 | self.model = [] 35 | 36 | def train_model(self, X, y): 37 | # Train machine learning model using scikit-learn 38 | self.model = RandomForestRegressor(n_estimators=100) 39 | self.model.fit(X, y) 40 | 41 | def predict(self, X): 42 | # Make predictions using trained model 43 | return self.model.predict(X) 44 | 45 | def evaluate_model(self, X, y): 46 | # Evaluate model performance using mean squared error 47 | return mean_squared_error(y, self.model.predict(X)) 48 | 49 | class Simulation: 50 | def __init__(self): 51 | self.simulation_data = [] 52 | 53 | def run_simulation(self, model, initial_conditions, time_step, total_time): 54 | # Run simulation using scipy's odeint function 55 | self.simulation_data = odeint(model, initial_conditions, np.arange(0, total_time, time_step)) 56 | 57 | def visualize_simulation(self): 58 | # Visualize simulation results using matplotlib 59 | plot(self.simulation_data) 60 | show() 61 | 62 | def data_analysis(file_path): 63 | # Perform data analysis using DataAnalysis class 64 | data_analysis = DataAnalysis() 65 | data_analysis.load_data(file_path) 66 | data_analysis.clean_data() 67 | data_analysis.visualize_data() 68 | 69 | def machine_learning(X, y): 70 | # Perform machine learning using MachineLearning class 71 | machine_learning = MachineLearning() 72 | machine_learning.train_model(X, y) 73 | return machine_learning.predict(X) 74 | 75 | def simulation(model, initial_conditions, time_step, total_time): 76 | # Perform simulation using Simulation class 77 | simulation = Simulation() 78 | simulation.run_simulation(model, initial_conditions, time_step, total_time) 79 | simulation.visualize_simulation() 80 | -------------------------------------------------------------------------------- /src/data/data_management.py: -------------------------------------------------------------------------------- 1 | # data_management.py 2 | 3 | import pandas as pd 4 | import matplotlib.pyplot as plt 5 | import plotly .express as px 6 | import numpy as np 7 | from sklearn.model_selection import train_test_split 8 | from sklearn.linear_model import LinearRegression 9 | from sklearn import metrics 10 | 11 | class DataManagement: 12 | def __init__(self): 13 | self.database = Database() 14 | self.data_visualization = DataVisualization() 15 | self.data_analysis = DataAnalysis() 16 | 17 | def run(self): 18 | # Run the data management system 19 | self.database.create_database() 20 | self.data_visualization.visualize_data() 21 | self.data_analysis.analyze_data() 22 | 23 | class Database: 24 | def __init__(self): 25 | self.data = None 26 | 27 | def create_database(self): 28 | # Create a database to store sensor data 29 | # For example, use a pandas DataFrame to store the data 30 | self.data = pd.DataFrame({ 31 | 'Sensor1': np.random.rand(100), 32 | 'Sensor2': np.random.rand(100), 33 | 'Sensor3': np.random.rand(100) 34 | }) 35 | 36 | class DataVisualization: 37 | def __init__(self): 38 | self.fig = None 39 | 40 | def visualize_data(self): 41 | # Visualize the data using matplotlib and plotly 42 | # For example, create a line plot of the sensor data 43 | self.fig = px.line(self.database.data, title='Sensor Data') 44 | self.fig.show() 45 | 46 | class DataAnalysis: 47 | def __init__(self): 48 | self.model = None 49 | 50 | def analyze_data(self): 51 | # Analyze the data using machine learning models 52 | # For example, use a linear regression model to predict the next sensor reading 53 | X = self.database.data[['Sensor1', 'Sensor2']] 54 | y = self.database.data['Sensor3'] 55 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 56 | self.model = LinearRegression() 57 | self.model.fit(X_train, y_train) 58 | y_pred = self.model.predict(X_test) 59 | print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) 60 | print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) 61 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) 62 | 63 | # database.py 64 | class Database: 65 | def __init__(self): 66 | self.data = None 67 | 68 | def create_database(self): 69 | # Create a database to store sensor data 70 | # For example, use a pandas DataFrame to store the data 71 | self.data = pd.DataFrame({ 72 | 'Sensor1': np.random.rand(100), 73 | 'Sensor2': np.random.rand(100), 74 | 'Sensor3': np.random.rand(100) 75 | }) 76 | 77 | # data_visualization.py 78 | class DataVisualization: 79 | def __init__(self): 80 | self.fig = None 81 | 82 | def visualize_data(self): 83 | # Visualize the data using matplotlib and plotly 84 | # For example, create a line plot of the sensor data 85 | self.fig = px.line(self.database.data, title='Sensor Data') 86 | self.fig.show() 87 | 88 | # data_analysis.py 89 | class DataAnalysis: 90 | def __init__(self): 91 | self.model = None 92 | 93 | def analyze_data(self): 94 | # Analyze the data using machine learning models 95 | # For example, use a linear regression model to predict the next sensor reading 96 | X = self.database.data[['Sensor1', 'Sensor2']] 97 | y = self.database.data['Sensor3'] 98 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 99 | self.model = LinearRegression() 100 | self.model.fit(X_train, y_train) 101 | y_pred = self.model.predict(X_test) 102 | print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) 103 | print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) 104 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) 105 | -------------------------------------------------------------------------------- /src/tests/test.py: -------------------------------------------------------------------------------- 1 | # tests/unit_tests/test_autonomous_system.py 2 | 3 | import unittest 4 | from autonomous_systems import AutonomousSystem 5 | 6 | class TestAutonomousSystem(unittest.TestCase): 7 | 8 | def test_decision_making(self): 9 | # Test the decision making module 10 | autonomous_system = AutonomousSystem() 11 | decision = autonomous_system.decision_making.make_decision() 12 | self.assertIsNotNone(decision) 13 | 14 | def test_navigation(self): 15 | # Test the navigation module 16 | autonomous_system = AutonomousSystem() 17 | path = autonomous_system.navigation.navigate() 18 | self.assertIsNotNone(path) 19 | 20 | def test_control(self): 21 | # Test the control module 22 | autonomous_system = AutonomousSystem() 23 | control_signal = autonomous_system.control.control_system() 24 | self.assertIsNotNone(control_signal) 25 | 26 | # tests/integration_tests/test_autonomous_system_integration.py 27 | 28 | import unittest 29 | from autonomous_systems import AutonomousSystem 30 | 31 | class TestAutonomousSystemIntegration(unittest.TestCase): 32 | 33 | def test_autonomous_system_integration(self): 34 | # Test the integration of the autonomous system 35 | autonomous_system = AutonomousSystem() 36 | autonomous_system.run() 37 | self.assertIsNotNone(autonomous_system.decision_making.decision) 38 | self.assertIsNotNone(autonomous_system.navigation.path) 39 | self.assertIsNotNone(autonomous_system.control.control_signal) 40 | 41 | # tests/system_tests/test_autonomous_system_system.py 42 | 43 | import unittest 44 | from autonomous_systems import AutonomousSystem 45 | 46 | class TestAutonomousSystemSystem(unittest.TestCase): 47 | 48 | def test_autonomous_system_system(self): 49 | # Test the system-level functionality of the autonomous system 50 | autonomous_system = AutonomousSystem() 51 | autonomous_system.run() 52 | self.assertIsNotNone(autonomous_system.decision_making.decision) 53 | self.assertIsNotNone(autonomous_system.navigation.path) 54 | self.assertIsNotNone(autonomous_system.control.control_signal) 55 | 56 | # tests/unit_tests/test_data_management.py 57 | 58 | import unittest 59 | from data_management import DataManagement 60 | 61 | class TestDataManagement(unittest.TestCase): 62 | 63 | def test_database_creation(self): 64 | # Test the database creation module 65 | data_management = DataManagement() 66 | data_management.database.create_database() 67 | self.assertIsNotNone(data_management.database.data) 68 | 69 | def test_data_visualization(self): 70 | # Test the data visualization module 71 | data_management = DataManagement() 72 | data_management.data_visualization.visualize_data() 73 | self.assertIsNotNone(data_management.data_visualization.fig) 74 | 75 | def test_data_analysis(self): 76 | # Test the data analysis module 77 | data_management = DataManagement() 78 | data_management.data_analysis.analyze_data() 79 | self.assertIsNotNone(data_management.data_analysis.model) 80 | 81 | # tests/integration_tests/test_data_management_integration.py 82 | 83 | import unittest 84 | from data_management import DataManagement 85 | 86 | class TestDataManagementIntegration(unittest.TestCase): 87 | 88 | def test_data_management_integration(self): 89 | # Test the integration of the data management system 90 | data_management = DataManagement() 91 | data_management.run() 92 | self.assertIsNotNone(data_management.database.data) 93 | self.assertIsNotNone(data_management.data_visualization.fig) 94 | self.assertIsNotNone(data_management.data_analysis.model) 95 | 96 | # tests/system_tests/test_data_management_system.py 97 | 98 | import unittest 99 | from data_management import DataManagement 100 | 101 | class TestDataManagementSystem(unittest.TestCase): 102 | 103 | def test_data_management_system(self): 104 | # Test the system-level functionality of the data management system 105 | data_management = DataManagement() 106 | data_management.run() 107 | self.assertIsNotNone(data_management.database.data) 108 | self.assertIsNotNone(data_management.data_visualization.fig) 109 | self.assertIsNotNone(data_management.data_analysis.model) 110 | -------------------------------------------------------------------------------- /src/autonomous/autonomous_systems.py: -------------------------------------------------------------------------------- 1 | # autonomous_systems.py 2 | 3 | import rospy 4 | import roslib 5 | import tf 6 | import numpy as np 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | 11 | class AutonomousSystem: 12 | def __init__(self): 13 | self.decision_making = DecisionMaking() 14 | self.navigation = Navigation() 15 | self.control = Control() 16 | 17 | def run(self): 18 | # Run the autonomous system 19 | self.decision_making.make_decision() 20 | self.navigation.navigate() 21 | self.control.control_system() 22 | 23 | class DecisionMaking: 24 | def __init__(self): 25 | self.decision = None 26 | 27 | def make_decision(self): 28 | # Make a decision based on sensor data and machine learning models 29 | # For example, use a random forest regressor to predict the next action 30 | from sklearn.ensemble import RandomForestRegressor 31 | X = np.array([[1, 2], [3, 4], [5, 6]]) # Sensor data 32 | y = np.array([7, 8, 9]) # Target values 33 | model = RandomForestRegressor(n_estimators=100) 34 | model.fit(X, y) 35 | self.decision = model.predict(np.array([[10, 11]])) # Predict the next action 36 | 37 | class Navigation: 38 | def __init__(self): 39 | self.path = None 40 | 41 | def navigate(self): 42 | # Navigate to the next location based on the decision 43 | # For example, use a simple model to simulate the navigation 44 | def model(state, t): 45 | return np.array([state[0] + state[1], state[1] - state[0]]) 46 | initial_conditions = np.array([1, 2]) 47 | time_step = 0.1 48 | total_time = 10 49 | self.path = odeint(model, initial_conditions, np.arange(0, total_time, time_step)) 50 | 51 | class Control: 52 | def __init__(self): 53 | self.control_signal = None 54 | 55 | def control_system(self): 56 | # Control the system based on the navigation path 57 | # For example, use a PID controller to generate a control signal 58 | from scipy.optimize import minimize 59 | def pid_controller(state, t): 60 | return np.array([state[0] + state[1], state[1] - state[0]]) 61 | initial_conditions = np.array([1, 2]) 62 | time_step = 0.1 63 | total_time = 10 64 | self.control_signal = minimize(pid_controller, initial_conditions, method="SLSQP").x 65 | 66 | # decision_making.py 67 | class DecisionMaking: 68 | def __init__(self): 69 | self.decision = None 70 | 71 | def make_decision(self): 72 | # Make a decision based on sensor data and machine learning models 73 | # For example, use a random forest regressor to predict the next action 74 | from sklearn.ensemble import RandomForestRegressor 75 | X = np.array([[1, 2], [3, 4], [5, 6]]) # Sensor data 76 | y = np.array([7, 8, 9]) # Target values 77 | model = RandomForestRegressor(n_estimators=100) 78 | model.fit(X, y) 79 | self.decision = model.predict(np.array([[10, 11]])) # Predict the next action 80 | 81 | # navigation.py 82 | class Navigation: 83 | def __init__(self): 84 | self.path = None 85 | 86 | def navigate(self): 87 | # Navigate to the next location based on the decision 88 | # For example, use a simple model to simulate the navigation 89 | def model(state, t): 90 | return np.array([state[0] + state[1], state[1] - state[0]]) 91 | initial_conditions = np.array([1, 2]) 92 | time_step = 0.1 93 | total_time = 10 94 | self.path = odeint(model, initial_conditions, np.arange(0, total_time, time_step)) 95 | 96 | # control.py 97 | class Control: 98 | def __init__(self): 99 | self.control_signal = None 100 | 101 | def control_system(self): 102 | # Control the system based on the navigation path 103 | # For example, use a PID controller to generate a control signal 104 | from scipy.optimize import minimize 105 | def pid_controller(state, t): 106 | return np.array([state[0] + state[1], state[1] - state[0]]) 107 | initial_conditions = np.array([1, 2]) 108 | time_step = 0.1 109 | total_time = 10 110 | self.control_signal = minimize(pid_controller, initial_conditions, method="SLSQP").x 111 | -------------------------------------------------------------------------------- /src/habitat/modules/food_production/food_production.py: -------------------------------------------------------------------------------- 1 | # food_production.py 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.ensemble import RandomForestRegressor 6 | from sklearn.metrics import mean_squared_error 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | import rospy 11 | import roslib 12 | import tf 13 | 14 | class FoodProductionSystem: 15 | def __init__(self, habitat): 16 | self.habitat = habitat 17 | self.hydroponics = Hydroponics() 18 | self.aeroponics = Aeroponics() 19 | self.algae_farming = AlgaeFarming() 20 | self.food_storage = FoodStorage() 21 | 22 | def monitor_food_production(self): 23 | hydroponic_yield = self.hydroponics.get_hydroponic_yield() 24 | aeroponic_yield = self.aeroponics.get_aeroponic_yield() 25 | algae_yield = self.algae_farming.get_algae_yield() 26 | total_yield = hydroponic_yield + aeroponic_yield + algae_yield 27 | return total_yield 28 | 29 | def control_food_production(self, total_yield): 30 | if total_yield < self.habitat.food_demand: 31 | self.hydroponics.activate_hydroponics() 32 | self.aeroponics.activate_aeroponics() 33 | self.algae_farming.activate_algae_farming() 34 | elif total_yield > self.habitat.food_demand: 35 | self.hydroponics.deactivate_hydroponics() 36 | self.aeroponics.deactivate_aeroponics() 37 | self.algae_farming.deactivate_algae_farming() 38 | 39 | def optimize_food_production(self): 40 | # Use machine learning model to optimize food production 41 | X = pd.DataFrame({'hydroponic_yield': [self.hydroponics.get_hydroponic_yield()], 42 | 'aeroponic_yield': [self.aeroponics.get_aeroponic_yield()], 43 | 'algae_yield': [self.algae_farming.get_algae_yield()]}) 44 | y = pd.DataFrame({'total_yield': [self.monitor_food_production()]}) 45 | model = RandomForestRegressor(n_estimators=100) 46 | model.fit(X, y) 47 | optimized_yield = model.predict(X) 48 | return optimized_yield 49 | 50 | class Hydroponics: 51 | def __init__(self): 52 | self.hydroponic_yield = 0 53 | self.hydroponic_system = [] 54 | 55 | def get_hydroponic_yield(self): 56 | return self.hydroponic_yield 57 | 58 | def activate_hydroponics(self): 59 | # Activate hydroponic system using ROS 60 | rospy.init_node('hydroponics_node') 61 | self.hydroponic_system = ['hydroponic_system_1', 'hydroponic_system_2', 'hydroponic_system_3'] 62 | self.hydroponic_yield = 10 63 | 64 | def deactivate_hydroponics(self): 65 | # Deactivate hydroponic system using ROS 66 | rospy.init_node('hydroponics_node') 67 | self.hydroponic_system = [] 68 | self.hydroponic_yield = 0 69 | 70 | class Aeroponics: 71 | def __init__(self): 72 | self.aeroponic_yield = 0 73 | self.aeroponic_system = [] 74 | 75 | def get_aeroponic_yield(self): 76 | return self.aeroponic_yield 77 | 78 | def activate_aeroponics(self): 79 | # Activate aeroponic system using ROS 80 | rospy.init_node('aeroponics_node') 81 | self.aeroponic_system = ['aeroponic_system_1', 'aeroponic_system_2', 'aeroponic_system_3'] 82 | self.aeroponic_yield = 15 83 | 84 | def deactivate_aeroponics(self): 85 | # Deactivate aeroponic system using ROS 86 | rospy.init_node('aeroponics_node') 87 | self.aeroponic_system = [] 88 | self.aeroponic_yield = 0 89 | 90 | class AlgaeFarming: 91 | def __init__(self): 92 | self.algae_yield = 0 93 | self.algae_farming_system = [] 94 | 95 | def get_algae_yield(self): 96 | return self.algae_yield 97 | 98 | def activate_algae_farming(self): 99 | # Activate algae farming system using ROS 100 | rospy.init_node('algae_farming_node') 101 | self.algae_farming_system = ['algae_farming_system_1', 'algae_farming_system_2', 'algae_farming_system_3'] 102 | self.algae_yield = 20 103 | 104 | def deactivate_algae_farming(self): 105 | # Deactivate algae farming system using ROS 106 | rospy.init_node('algae_farming_node') 107 | self.algae_farming_system = [] 108 | self.algae_yield = 0 109 | 110 | class FoodStorage: 111 | def __init__(self): 112 | self.food_storage = 0 113 | self.food_storage_system = [] 114 | 115 | def get_food_storage(self): 116 | return self.food_storage 117 | 118 | def store_food(self, total_yield): 119 | # Store food in storage system using ROS 120 | rospy.init_node('food_storage_node') 121 | self.food_storage_system = ['food_storage_system_1', 'food_storage_system_2', 'food_storage_system_3'] 122 | self.food_storage = total_yield 123 | 124 | def retrieve_food(self): 125 | # Retrieve food from storage system using ROS 126 | rospy.init_node('food_storage_node') 127 | self.food_storage_system = [] 128 | self.food_storage = 0 129 | -------------------------------------------------------------------------------- /src/habitat/systems/systems.py: -------------------------------------------------------------------------------- 1 | # systems.py 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.ensemble import RandomForestRegressor 6 | from sklearn.metrics import mean_squared_error 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | import rospy 11 | import roslib 12 | import tf 13 | 14 | class HabitatHub: 15 | def __init__(self): 16 | self.airlock_system = AirlockSystem() 17 | self.communication_system = CommunicationSystem() 18 | self.navigation_system = NavigationSystem() 19 | self.propulsion_system = PropulsionSystem() 20 | 21 | def monitor_systems(self): 22 | airlock_status = self.airlock_system.get_airlock_status() 23 | communication_status = self.communication_system.get_communication_status() 24 | navigation_status = self.navigation_system.get_navigation_status() 25 | propulsion_status = self.propulsion_system.get_propulsion_status() 26 | return airlock_status, communication_status, navigation_status, propulsion_status 27 | 28 | def control_systems(self, airlock_status, communication_status, navigation_status, propulsion_status): 29 | if airlock_status < self.airlock_system.airlock_requirement: 30 | self.airlock_system.activate_airlock() 31 | elif airlock_status > self.airlock_system.airlock_requirement: 32 | self.airlock_system.deactivate_airlock() 33 | 34 | if communication_status < self.communication_system.communication_requirement: 35 | self.communication_system.activate_communication() 36 | elif communication_status > self.communication_system.communication_requirement: 37 | self.communication_system.deactivate_communication() 38 | 39 | if navigation_status < self.navigation_system.navigation_requirement: 40 | self.navigation_system.activate_navigation() 41 | elif navigation_status > self.navigation_system.navigation_requirement: 42 | self.navigation_system.deactivate_navigation() 43 | 44 | if propulsion_status < self.propulsion_system.propulsion_requirement: 45 | self.propulsion_system.activate_propulsion() 46 | elif propulsion_status > self.propulsion_system.propulsion_requirement: 47 | self.propulsion_system.deactivate_propulsion() 48 | 49 | def optimize_systems(self): 50 | # Use machine learning model to optimize systems 51 | X = pd.DataFrame({'airlock_status': [self.airlock_system.get_airlock_status()], 52 | 'communication_status': [self.communication_system.get_communication_status()], 53 | 'navigation_status': [self.navigation_system.get_navigation_status()], 54 | 'propulsion_status': [self.propulsion_system.get_propulsion_status()]}) 55 | y = pd.DataFrame({'system_performance': [self.monitor_systems()]}) 56 | model = RandomForestRegressor(n_estimators=100) 57 | model.fit(X, y) 58 | optimized_performance = model.predict(X) 59 | return optimized_performance 60 | 61 | class AirlockSystem: 62 | def __init__(self): 63 | self.airlock_status = 0 64 | self.airlock_requirement = 100 65 | self.airlock_system = [] 66 | 67 | def get_airlock_status(self): 68 | return self.airlock_status 69 | 70 | def activate_airlock(self): 71 | # Activate airlock system using ROS 72 | rospy.init_node('airlock_node') 73 | self.airlock_system = ['airlock_system_1', 'airlock_system_2', 'airlock_system_3'] 74 | self.airlock_status = 100 75 | 76 | def deactivate_airlock(self): 77 | # Deactivate airlock system using ROS 78 | rospy.init_node('airlock_node') 79 | self.airlock_system = [] 80 | self.airlock_status = 0 81 | 82 | class CommunicationSystem: 83 | def __init__(self): 84 | self.communication_status = 0 85 | self.communication_requirement = 100 86 | self.communication_system = [] 87 | 88 | def get_communication_status(self): 89 | return self.communication_status 90 | 91 | def activate_communication(self): 92 | # Activate communication system using ROS 93 | rospy.init_node('communication_node') 94 | self.communication_system = ['communication_system_1', 'communication_system_2', 'communication_system_3'] 95 | self.communication_status = 100 96 | 97 | def deactivate_communication(self): 98 | # Deactivate communication system using ROS 99 | rospy.init_node('communication_node') 100 | self.communication_system = [] 101 | self.communication_status = 0 102 | 103 | class NavigationSystem: 104 | def __init__(self): 105 | self.navigation_status = 0 106 | self.navigation_requirement = 100 107 | self.navigation_system = [] 108 | 109 | def get_navigation_status(self): 110 | return self.navigation_status 111 | 112 | def activate_navigation(self): 113 | # Activate navigation system using ROS 114 | rospy.init_node('navigation_node') 115 | self.navigation_system = ['navigation_system_1', 'navigation_system_2', 'navigation_system_3'] 116 | self.navigation_status = 100 117 | 118 | def deactivate_navigation(self): 119 | # Deactivate navigation system using ROS 120 | rospy.init_node('navigation_node') 121 | self.navigation_system = [] 122 | self.navigation_status = 0 123 | 124 | class PropulsionSystem: 125 | def __init__(self): 126 | self.propulsion_status = 0 127 | self.propulsion_requirement = 100 128 | self.propulsion_system = [] 129 | 130 | def get_propulsion_status(self): 131 | return self.propulsion_status 132 | 133 | def activate_propulsion(self): 134 | # Activate propulsion system using ROS 135 | rospy.init_node('propulsion_node') 136 | self.propulsion_system = ['propulsion_system_1', 'propulsion_system_2','propulsion_system_3'] 137 | self.propulsion_status = 100 138 | 139 | def deactivate_propulsion(self): 140 | # Deactivate propulsion system using ROS 141 | rospy.init_node('propulsion_node') 142 | self.propulsion_system = [] 143 | self.propulsion_status = 0 144 | -------------------------------------------------------------------------------- /src/habitat/modules/energy_generation/energy_generation.py: -------------------------------------------------------------------------------- 1 | # energy_generation.py 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.ensemble import RandomForestRegressor 6 | from sklearn.metrics import mean_squared_error 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | import rospy 11 | import roslib 12 | import tf 13 | 14 | class EnergyGenerationSystem: 15 | def __init__(self, habitat): 16 | self.habitat = habitat 17 | self.solar_panels = SolarPanels() 18 | self.wind_turbines = WindTurbines() 19 | self.nuclear_reactors = NuclearReactors() 20 | self.fuel_cells = FuelCells() 21 | self.energy_storage = EnergyStorage() 22 | 23 | def monitor_energy_generation(self): 24 | solar_energy = self.solar_panels.get_solar_energy() 25 | wind_energy = self.wind_turbines.get_wind_energy() 26 | nuclear_energy = self.nuclear_reactors.get_nuclear_energy() 27 | fuel_cell_energy = self.fuel_cells.get_fuel_cell_energy() 28 | total_energy = solar_energy + wind_energy + nuclear_energy + fuel_cell_energy 29 | return total_energy 30 | 31 | def control_energy_generation(self, total_energy): 32 | if total_energy < self.habitat.energy_demand: 33 | self.solar_panels.activate_solar_panels() 34 | self.wind_turbines.activate_wind_turbines() 35 | self.nuclear_reactors.activate_nuclear_reactors() 36 | self.fuel_cells.activate_fuel_cells() 37 | elif total_energy > self.habitat.energy_demand: 38 | self.solar_panels.deactivate_solar_panels() 39 | self.wind_turbines.deactivate_wind_turbines() 40 | self.nuclear_reactors.deactivate_nuclear_reactors() 41 | self.fuel_cells.deactivate_fuel_cells() 42 | 43 | def optimize_energy_generation(self): 44 | # Use machine learning model to optimize energy generation 45 | X = pd.DataFrame({'solar_energy': [self.solar_panels.get_solar_energy()], 46 | 'wind_energy': [self.wind_turbines.get_wind_energy()], 47 | 'nuclear_energy': [self.nuclear_reactors.get_nuclear_energy()], 48 | 'fuel_cell_energy': [self.fuel_cells.get_fuel_cell_energy()]}) 49 | y = pd.DataFrame({'total_energy': [self.monitor_energy_generation()]}) 50 | model = RandomForestRegressor(n_estimators=100) 51 | model.fit(X, y) 52 | optimized_energy = model.predict(X) 53 | return optimized_energy 54 | 55 | class SolarPanels: 56 | def __init__(self): 57 | self.solar_energy = 0 58 | self.solar_panel_system = [] 59 | 60 | def get_solar_energy(self): 61 | return self.solar_energy 62 | 63 | def activate_solar_panels(self): 64 | # Activate solar panel system using ROS 65 | rospy.init_node('solar_panels_node') 66 | self.solar_panel_system = ['solar_panel_system_1', 'solar_panel_system_2', 'solar_panel_system_3'] 67 | self.solar_energy = 100 68 | 69 | def deactivate_solar_panels(self): 70 | # Deactivate solar panel system using ROS 71 | rospy.init_node('solar_panels_node') 72 | self.solar_panel_system = [] 73 | self.solar_energy = 0 74 | 75 | class WindTurbines: 76 | def __init__(self): 77 | self.wind_energy = 0 78 | self.wind_turbine_system = [] 79 | 80 | def get_wind_energy(self): 81 | return self.wind_energy 82 | 83 | def activate_wind_turbines(self): 84 | # Activate wind turbine system using ROS 85 | rospy.init_node('wind_turbines_node') 86 | self.wind_turbine_system = ['wind_turbine_system_1', 'wind_turbine_system_2', 'wind_turbine_system_3'] 87 | self.wind_energy = 150 88 | 89 | def deactivate_wind_turbines(self): 90 | # Deactivate wind turbine system using ROS 91 | rospy.init_node('wind_turbines_node') 92 | self.wind_turbine_system = [] 93 | self.wind_energy = 0 94 | 95 | class NuclearReactors: 96 | def __init__(self): 97 | self.nuclear_energy = 0 98 | self.nuclear_reactor_system = [] 99 | 100 | def get_nuclear_energy(self): 101 | return self.nuclear_energy 102 | 103 | def activate_nuclear_reactors(self): 104 | # Activate nuclear reactor system using ROS 105 | rospy.init_node('nuclear_reactors_node') 106 | self.nuclear_reactor_system = ['nuclear_reactor_system_1', 'nuclear_reactor_system_2', 'nuclear_reactor_system_3'] 107 | self.nuclear_energy = 200 108 | 109 | def deactivate_nuclear_reactors(self): 110 | # Deactivate nuclear reactor system using ROS 111 | rospy.init_node('nuclear_reactors_node') 112 | self.nuclear_reactor_system = [] 113 | self.nuclear_energy = 0 114 | 115 | class FuelCells: 116 | def __init__(self): 117 | self.fuel_cell_energy = 0 118 | self.f uel_cell_system = [] 119 | 120 | def get_fuel_cell_energy(self): 121 | return self.fuel_cell_energy 122 | 123 | def activate_fuel_cells(self): 124 | # Activate fuel cell system using ROS 125 | rospy.init_node('fuel_cells_node') 126 | self.fuel_cell_system = ['fuel_cell_system_1', 'fuel_cell_system_2', 'fuel_cell_system_3'] 127 | self.fuel_cell_energy = 250 128 | 129 | def deactivate_fuel_cells(self): 130 | # Deactivate fuel cell system using ROS 131 | rospy.init_node('fuel_cells_node') 132 | self.fuel_cell_system = [] 133 | self.fuel_cell_energy = 0 134 | 135 | class EnergyStorage: 136 | def __init__(self): 137 | self.energy_storage = 0 138 | self.energy_storage_system = [] 139 | 140 | def get_energy_storage(self): 141 | return self.energy_storage 142 | 143 | def store_energy(self, total_energy): 144 | # Store energy in storage system using ROS 145 | rospy.init_node('energy_storage_node') 146 | self.energy_storage_system = ['energy_storage_system_1', 'energy_storage_system_2', 'energy_storage_system_3'] 147 | self.energy_storage = total_energy 148 | 149 | def retrieve_energy(self): 150 | # Retrieve energy from storage system using ROS 151 | rospy.init_node('energy_storage_node') 152 | self.energy_storage_system = [] 153 | self.energy_storage = 0 154 | -------------------------------------------------------------------------------- /src/habitat/modules/robotics/robotics.py: -------------------------------------------------------------------------------- 1 | # robotics.py 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.ensemble import RandomForestClassifier 6 | from sklearn.metrics import accuracy_score 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | import rospy 11 | import roslib 12 | import tf 13 | from geometry_msgs.msg import PoseStamped 14 | from sensor_msgs.msg import JointState 15 | from control_msgs.msg import JointTrajectory 16 | from trajectory_msgs.msg import JointTrajectoryPoint 17 | 18 | class RoboticsSystem: 19 | def __init__(self, habitat): 20 | self.habitat = habitat 21 | self.robot_arm = RobotArm() 22 | self.mobility_system = MobilitySystem() 23 | self.sensors = Sensors() 24 | self.actuators = Actuators() 25 | 26 | def monitor_robot_status(self): 27 | robot_arm_status = self.robot_arm.get_robot_arm_status() 28 | mobility_system_status = self.mobility_system.get_mobility_system_status() 29 | sensors_status = self.sensors.get_sensors_status() 30 | actuators_status = self.actuators.get_actuators_status() 31 | return robot_arm_status, mobility_system_status, sensors_status, actuators_status 32 | 33 | def control_robot(self, robot_arm_status, mobility_system_status, sensors_status, actuators_status): 34 | if robot_arm_status == 'active': 35 | self.robot_arm.activate_robot_arm() 36 | elif robot_arm_status == 'inactive': 37 | self.robot_arm.deactivate_robot_arm() 38 | 39 | if mobility_system_status == 'active': 40 | self.mobility_system.activate_mobility_system() 41 | elif mobility_system_status == 'inactive': 42 | self.mobility_system.deactivate_mobility_system() 43 | 44 | if sensors_status == 'active': 45 | self.sensors.activate_sensors() 46 | elif sensors_status == 'inactive': 47 | self.sensors.deactivate_sensors() 48 | 49 | if actuators_status == 'active': 50 | self.actuators.activate_actuators() 51 | elif actuators_status == 'inactive': 52 | self.actuators.deactivate_actuators() 53 | 54 | def optimize_robot_performance(self): 55 | # Use machine learning model to optimize robot performance 56 | X = pd.DataFrame({'robot_arm_status': [self.robot_arm.get_robot_arm_status()], 57 | 'mobility_system_status': [self.mobility_system.get_mobility_system_status()], 58 | 'sensors_status': [self.sensors.get_sensors_status()], 59 | 'actuators_status': [self.actuators.get_actuators_status()]}) 60 | y = pd.DataFrame({'robot_performance': [self.monitor_robot_status()]}) 61 | model = RandomForestClassifier(n_estimators=100) 62 | model.fit(X, y) 63 | optimized_performance = model.predict(X) 64 | return optimized_performance 65 | 66 | class RobotArm: 67 | def __init__(self): 68 | self.robot_arm_status = 'inactive' 69 | self.joint_states = JointState() 70 | self.joint_trajectory = JointTrajectory() 71 | 72 | def get_robot_arm_status(self): 73 | return self.robot_arm_status 74 | 75 | def activate_robot_arm(self): 76 | # Activate robot arm using ROS 77 | rospy.init_node('robot_arm_node') 78 | self.joint_states.header.stamp = rospy.Time.now() 79 | self.joint_states.name = ['joint1', 'joint2', 'joint3'] 80 | self.joint_states.position = [0.5, 0.5, 0.5] 81 | self.joint_states.velocity = [0.1, 0.1, 0.1] 82 | self.joint_states.effort = [10, 10, 10] 83 | self.joint_trajectory.joint_names = ['joint1', 'joint2', 'joint3'] 84 | self.joint_trajectory.points = [JointTrajectoryPoint(positions=[0.5, 0.5, 0.5], velocities=[0.1, 0.1, 0.1], accelerations=[0.1, 0.1, 0.1], time_from_start=rospy.Duration(1.0))] 85 | 86 | def deactivate_robot_arm(self): 87 | # Deactivate robot arm using ROS 88 | rospy.init_node('robot_arm_node') 89 | self.joint_states.header.stamp = rospy.Time.now() 90 | self.joint_states.name = ['joint1', 'joint2', 'joint3'] 91 | self.joint_states.position = [0.0, 0.0, 0.0] 92 | self.joint_states.velocity = [0.0, 0.0, 0.0] 93 | self.joint_states.effort = [0.0, 0.0, 0.0] 94 | self.joint_trajectory.joint_names = ['joint1', 'joint2', 'joint3'] 95 | self.joint_trajectory.points = [JointTrajectoryPoint(positions=[0.0, 0.0, 0.0], velocities=[0.0, 0.0, 0.0], accelerations=[0.0, 0 .0, 0.0], time_from_start=rospy.Duration(1.0))] 96 | 97 | class MobilitySystem: 98 | def __init__(self): 99 | self.mobility_system_status = 'inactive' 100 | self.pose_stamped = PoseStamped() 101 | 102 | def get_mobility_system_status(self): 103 | return self.mobility_system_status 104 | 105 | def activate_mobility_system(self): 106 | # Activate mobility system using ROS 107 | rospy.init_node('mobility_system_node') 108 | self.pose_stamped.header.stamp = rospy.Time.now() 109 | self.pose_stamped.pose.position.x = 1.0 110 | self.pose_stamped.pose.position.y = 1.0 111 | self.pose_stamped.pose.position.z = 1.0 112 | self.pose_stamped.pose.orientation.x = 0.0 113 | self.pose_stamped.pose.orientation.y = 0.0 114 | self.pose_stamped.pose.orientation.z = 0.0 115 | self.pose_stamped.pose.orientation.w = 1.0 116 | 117 | def deactivate_mobility_system(self): 118 | # Deactivate mobility system using ROS 119 | rospy.init_node('mobility_system_node') 120 | self.pose_stamped.header.stamp = rospy.Time.now() 121 | self.pose_stamped.pose.position.x = 0.0 122 | self.pose_stamped.pose.position.y = 0.0 123 | self.pose_stamped.pose.position.z = 0.0 124 | self.pose_stamped.pose.orientation.x = 0.0 125 | self.pose_stamped.pose.orientation.y = 0.0 126 | self.pose_stamped.pose.orientation.z = 0.0 127 | self.pose_stamped.pose.orientation.w = 1.0 128 | 129 | class Sensors: 130 | def __init__(self): 131 | self.sensors_status = 'inactive' 132 | 133 | def get_sensors_status(self): 134 | return self.sensors_status 135 | 136 | def activate_sensors(self): 137 | # Activate sensors using ROS 138 | rospy.init_node('sensors_node') 139 | self.sensors_status = 'active' 140 | 141 | def deactivate_sensors(self): 142 | # Deactivate sensors using ROS 143 | rospy.init_node('sensors_node') 144 | self.sensors_status = 'inactive' 145 | 146 | class Actuators: 147 | def __init__(self): 148 | self.actuators_status = 'inactive' 149 | 150 | def get_actuators_status(self): 151 | return self.actuators_status 152 | 153 | def activate_actuators(self): 154 | # Activate actuators using ROS 155 | rospy.init_node('actuators_node') 156 | self.actuators_status = 'active' 157 | 158 | def deactivate_actuators(self): 159 | # Deactivate actuators using ROS 160 | rospy.init_node('actuators_node') 161 | self.actuators_status = 'inactive' 162 | -------------------------------------------------------------------------------- /src/habitat/modules/life_support/life_support.py: -------------------------------------------------------------------------------- 1 | # life_support.py 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.ensemble import RandomForestRegressor 6 | from sklearn.metrics import mean_squared_error 7 | from scipy.optimize import minimize 8 | from scipy.integrate import odeint 9 | from matplotlib.pyplot import plot, show, xlabel, ylabel, title 10 | import rospy 11 | import roslib 12 | import tf 13 | 14 | class LifeSupportSystem: 15 | def __init__(self, habitat): 16 | self.habitat = habitat 17 | self.air_supply = AirSupply() 18 | self.water_supply = WaterSupply() 19 | self.waste_management = WasteManagement() 20 | self.temperature_control = TemperatureControl() 21 | self.humidity_control = HumidityControl() 22 | self.lighting_control = LightingControl() 23 | self.air_quality_control = AirQualityControl() 24 | 25 | def monitor_life_support(self): 26 | air_quality = self.air_supply.get_air_quality() 27 | water_quality = self.water_supply.get_water_quality() 28 | waste_level = self.waste_management.get_waste_level() 29 | temperature = self.temperature_control.get_temperature() 30 | humidity = self.humidity_control.get_humidity() 31 | lighting_level = self.lighting_control.get_lighting_level() 32 | air_quality_level = self.air_quality_control.get_air_quality_level() 33 | return air_quality, water_quality, waste_level, temperature, humidity, lighting_level, air_quality_level 34 | 35 | def control_life_support(self, air_quality, water_quality, waste_level, temperature, humidity, lighting_level, air_quality_level): 36 | if air_quality < self.habitat.air_quality_requirement: 37 | self.air_supply.activate_air_supply() 38 | elif air_quality > self.habitat.air_quality_requirement: 39 | self.air_supply.deactivate_air_supply() 40 | 41 | if water_quality < self.habitat.water_quality_requirement: 42 | self.water_supply.activate_water_supply() 43 | elif water_quality > self.habitat.water_quality_requirement: 44 | self.water_supply.deactivate_water_supply() 45 | 46 | if waste_level > self.habitat.waste_level_requirement: 47 | self.waste_management.activate_waste_management() 48 | elif waste_level < self.habitat.waste_level_requirement: 49 | self.waste_management.deactivate_waste_management() 50 | 51 | if temperature < self.habitat.temperature_requirement: 52 | self.temperature_control.activate_temperature_control() 53 | elif temperature > self.habitat.temperature_requirement: 54 | self.temperature_control.deactivate_temperature_control() 55 | 56 | if humidity < self.habitat.humidity_requirement: 57 | self.humidity_control.activate_humidity_control() 58 | elif humidity > self.habitat.humidity_requirement: 59 | self.humidity_control.deactivate_humidity_control() 60 | 61 | if lighting_level < self.habitat.lighting_level_requirement: 62 | self.lighting_control.activate_lighting_control() 63 | elif lighting_level > self.habitat.lighting_level_requirement: 64 | self.lighting_control.deactivate_lighting_control() 65 | 66 | if air_quality_level < self.habitat.air_quality_level_requirement: 67 | self.air_quality_control.activate_air_quality_control() 68 | elif air_quality_level > self.habitat.air_quality_level_requirement: 69 | self.air_quality_control.deactivate_air_quality_control() 70 | 71 | def optimize_life_support(self): 72 | # Use machine learning model to optimize life support 73 | X = pd.DataFrame({'air_quality': [self.air_supply.get_air_quality()], 74 | 'water_quality': [self.water_supply.get_water_quality()], 75 | 'waste_level': [self.waste_management.get_waste_level()], 76 | 'temperature': [self.temperature_control.get_temperature()], 77 | 'humidity': [self.humidity_control.get_humidity()], 78 | 'lighting_level': [self.lighting_control.get_lighting_level()], 79 | 'air_quality_level': [self.air_quality_control.get_air_quality_level()]}) 80 | y = pd.DataFrame({'life_support_performance': [self.monitor_life_support()]}) 81 | model = RandomForestRegressor(n_estimators=100) 82 | model.fit(X, y) 83 | optimized_performance = model.predict(X) 84 | return optimized_performance 85 | 86 | class AirSupply: 87 | def __init__(self): 88 | self.air_quality = 0 89 | self.air_supply_system = [] 90 | 91 | def get_air_quality(self): 92 | return self.air_quality 93 | 94 | def activate_air_supply(self): 95 | # Activate air supply system using ROS 96 | rospy.init_node('air_supply_node') 97 | self.air_supply_system = ['air_supply_system_1', 'air_supply_system_2', 'air_supply_system_3'] 98 | self.air_quality = 100 99 | 100 | def deactivate_air_supply(self): 101 | # Deactivate air supply system using ROS 102 | rospy.init_node('air_supply_node') 103 | self.air_supply_system = [] 104 | self.air_quality = 0 105 | 106 | class WaterSupply: 107 | def __init__(self): 108 | self.water_quality = 0 109 | self.water_supply_system = [] 110 | 111 | def get_water_quality(self): 112 | return self.water_quality 113 | 114 | def activate_water_supply(self): 115 | # Activate water supply system using ROS 116 | rospy.init_node('water_supply_node') 117 | self.water_supply_system = ['water_supply_system_1', 'water_supply_system_2', 'water_supply_system_3'] 118 | self.water_quality = 100 119 | 120 | def deactivate_water_supply(self): 121 | # Deactivate water supply system using ROS 122 | rospy.init_node('water_supply_node') 123 | self.water_supply_system = [] 124 | self.water_quality = 0 125 | 126 | class WasteManagement: 127 | def __init__(self): 128 | self.waste_level = 0 129 | self.waste_management_system = [] 130 | 131 | def get_waste_level(self): 132 | return self.waste_level 133 | 134 | def activate_waste_management(self): 135 | # Activate waste management system using ROS 136 | rospy.init_node('waste_management_node') 137 | self.waste_management_system = ['waste_management_system_1', 'waste_management_system_2', 'waste_management_system_3'] 138 | self.waste_level = 50 139 | 140 | def deactivate_waste_management(self): 141 | # Deactivate waste management system using ROS 142 | rospy.init_node('waste_management_node') 143 | self.waste_management_system = [] 144 | self.waste_level = 0 145 | 146 | class TemperatureControl: 147 | def __init__(self): 148 | self.temperature = 0 149 | self.temperature_control_system = [] 150 | 151 | def get_temperature(self): 152 | return self.temperature 153 | 154 | def activate_temperature_control(self): 155 | # Activate temperature control system using ROS 156 | rospy.init_node('temperature_control_node') 157 | self.temperature_control_system = ['temperature_control_system_1', 'temperature_control_system_2', 'temperature_control_system_3'] 158 | self.temperature = 22 159 | 160 | def deactivate_temperature_control(self): 161 | # Deactivate temperature control system using ROS 162 | rospy.init_node('temperature_control_node') 163 | self.temperature_control_system = [] 164 | self.temperature = 0 165 | 166 | class HumidityControl: 167 | def __init__(self): 168 | self.humidity = 0 169 | self.humidity_control_system = [] 170 | 171 | def get_humidity(self): 172 | return self.humidity 173 | 174 | def activate_humidity_control(self): 175 | # Activate humidity control system using ROS 176 | rospy.init_node('humidity_control_node') 177 | self.humidity_control_system = ['humidity_control_system_1', 'humidity_control_system_2', 'humidity_control_system_3'] 178 | self.humidity = 50 179 | 180 | def deactivate_humidity_control(self): 181 | # Deactivate humidity control system using ROS 182 | rospy.init_node('humidity_control_node') 183 | self.humidity_control_system = [] 184 | self.humidity = 0 185 | 186 | class LightingControl: 187 | def __init__(self): 188 | self.lighting_level = 0 189 | self.lighting_control_system = [] 190 | 191 | def get_lighting_level(self): 192 | return self.lighting_level 193 | 194 | def activate_lighting_control(self): 195 | # Activate lighting control system using ROS 196 | rospy.init_node('lighting_control_node') 197 | self.lighting_control_system = ['lighting_control_system_1', 'lighting_control_system_2', 'lighting_control_system_3'] 198 | self.lighting_level = 100 199 | 200 | def deactivate_lighting_control(self): 201 | # Deactivate lighting control system using ROS 202 | rospy.init_node('lighting_control_node') 203 | self.lighting_control_system = [] 204 | self.lighting_level = 0 205 | 206 | class AirQualityControl: 207 | def __init__(self): 208 | self.air_quality_level = 0 209 | self.air_quality_control_system = [] 210 | 211 | def get_air_quality_level(self): 212 | return self.air_quality_level 213 | 214 | def activate_air_quality_control(self): 215 | # Activate air quality control system using ROS 216 | rospy.init_node('air_quality_control_node') 217 | self.air_quality_control_system = ['air_quality_control_system_1', 'air_quality_control_system_2', 'air_quality_control_system_3'] 218 | self.air_quality_level = 100 219 | 220 | def deactivate_air_quality_control(self): 221 | # Deactivate air quality control system using ROS 222 | rospy.init_node('air_quality_control_node') 223 | self.air_quality_control_system = [] 224 | self.air_quality_level = 0 225 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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