rlab-cli 1.0.0

RLab Platform CLI - Digital Twins for Educational Robotics powered by Nueroid

RLab CLI

A comprehensive command-line interface for the RLab Platform, providing digital twin capabilities for educational robotics powered by the Nueroid framework.

Features

• Digital Twin Framework: Complete Model/Shadow/Twin architecture using abstract factory pattern
• Educational Robotics: Specialized for robotics learning and research environments
• Multi-User Support: Role-based access for students, professors, administrators, and industry partners
• COE Compliance: Built-in reporting for Center of Excellence requirements
• Interactive CLI: Easy-to-use command-line interface for all operations

Installation

From PyPI (when published)

pip install rlab-cli

From Source

git clone https://github.com/Rosversity/rlab-cli.git
cd rlab-cli
pip install -e .

Development Installation

git clone https://github.com/Rosversity/rlab-cli.git
cd rlab-cli
pip install -e .[dev]

Quick Start

After installation, you can start the RLab CLI:

rlab
# or
rlab-cli

This will launch the interactive command-line interface where you can:

1. Initialize RLab Facility - Set up your educational facility
2. User Management - Register and manage users (students, professors, etc.)
3. Robotics Lab Management - Add and manage robotics assets
4. Digital Twin Operations - Interact with digital twins
5. Learning & Research - Create learning modules and research projects
6. COE Reports - Generate compliance reports
7. System Information - View system status

Architecture

RLab CLI is built on the Nueroid Digital Twin Framework using an abstract factory pattern:

• Model: Static data, UI widgets, 3D assets, and data tables
• Shadow: Workflows, hardware interfaces, and process management
• Twin: Dashboard widgets, user interactions, and real-time data

Digital Twin Hierarchy

• Facility: Educational institution level
• Product: Major systems (software/hardware platforms)
• System: Functional systems (user management, lab management)
• Asset: Individual robotics equipment
• Component: Learning modules and components
• Process: Executable workflows

Example Usage

from rlab_cli import RLabPlatform, UserType

# Initialize platform
rlab = RLabPlatform()
facility = rlab.initialize_rlab_facility("My University RLab")

# Register users
rlab.register_user("prof_smith", UserType.PROFESSOR)
rlab.register_user("student_jane", UserType.STUDENT)

# Add robotics assets
vacuum_robot = rlab.add_robotics_asset("Main Lab", "CleanBot 3000", "Cleaning Robot")
vacuum_robot.model.static_data["cost"] = 50000
vacuum_robot.model.static_data["coe_component"] = "ATL Consumables"

# Generate COE report
report = rlab.generate_coe_report()
print(f"Total Allocation: ₹{report['total_allocation']:,}")

User Types

• School Admin: Full administrative access
• Management: Oversight and reporting capabilities
• Professor: Course and research management
• Student: Learning and project participation
• Industry: Partnership and collaboration features
• Research Partner: Research collaboration tools
• Vendor: Equipment and service management
• Developer: Technical development access

Digital Twin Types

• Component: Individual learning components
• Asset: Physical robotics equipment
• System: Functional systems and subsystems
• Product: Complete product platforms
• Facility: Institutional level organization
• Process: Executable workflow processes

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Support

For questions and support:

• Create an issue on GitHub
• Email: [e-mail]

Acknowledgments

• Built with the Nueroid Digital Twin Framework
• Designed for educational robotics and COE compliance
• Developed by the Rosversity team