cyberwave 0.1.1

A powerful Python client for controlling various types of robots through simple, AI-driven APIs

CyberWave

CyberWave is a powerful Python client for controlling various types of robots through simple, AI-driven APIs. The platform provides a unified interface for interacting with different robotic systems, from aerial drones to industrial robotic arms, making it easy to implement complex robotic operations with minimal code.

Features

• Unified Robot Control: Control different types of robots (drones, robotic arms) through a single, consistent API
• AI-Powered Operations: Leverage machine learning for complex tasks like object detection and autonomous navigation
• Sensor Integration: Easy integration with various sensors (cameras, force sensors, etc.)
• Video-Based Training: Train models using video demonstrations for complex tasks
• Safety First: Built-in safety checks and error handling for all operations

Supported Robots

• Aerial Robots: DJI Tello and compatible drones
• Robotic Arms: KUKA KR3 Agilus and compatible industrial arms
• More robot types coming soon!

Installation

pip install cyberwave

Quick Start

Controlling a Drone

from cyberwave import Robot

# Initialize and connect to a drone
drone = Robot("dji/tello")
drone.connect(ip_address="192.168.1.10")

# Execute autonomous flight operations
drone.takeoff()
drone.scan_environment()
location = drone.find_object(instruction='red landing target')
drone.fly_to(location)
drone.land()

Controlling a Robotic Arm

from cyberwave import Robot, VideoTrainer, perform_welding
import asyncio

# Initialize and connect to a robotic arm
arm = Robot("kuka/kr3_agilus")
arm.connect(ip_address="192.168.1.100")
arm.initialize_sensors(["camera", "force_sensor"])

# Train the arm using video demonstrations
trainer = VideoTrainer(model_type="welding")
trainer.train_from_videos(["weld_example1.mp4", "weld_example2.mp4"])

# Execute the trained welding operation
asyncio.run(perform_welding(arm, trainer.model))

Key Features

1. Unified Robot Control

• Single API for different robot types
• Consistent command structure across platforms
• Automatic robot type detection and appropriate safety measures

2. AI-Powered Operations

• Computer vision for object detection and tracking
• Autonomous navigation and path planning
• Machine learning for complex task execution
• Video-based training for new operations

3. Sensor Integration

• Easy sensor initialization and calibration
• Support for multiple sensor types:
  • Cameras
  • Force sensors
  • LIDAR
  • More coming soon

4. Safety Features

• Automatic safety checks before operations
• Error handling and recovery procedures
• Collision avoidance
• Emergency stop capabilities

Development

To set up the development environment:

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install development dependencies:

pip install -r requirements.txt

Documentation

For detailed documentation, visit our documentation site.

License

MIT License

Contributing

We welcome contributions! Please see our contributing guide for details.