kling-motio-control 1768444.568.616
High-quality integration for https://supermaker.ai/blog/what-is-kling-motion-control-ai-how-to-use-motion-control-ai-free-online/
Requires Python: >=3.6

# kling-motio-control

An automated Python library designed to demonstrate kling-motio-control capabilities and provide a simplified interface for motion control tasks. This package offers a convenient way to explore and integrate with the core functionalities described at https://supermaker.ai/blog/what-is-kling-motion-control-ai-how-to-use-motion-control-ai-free-online/.

## Installation

You can install `kling-motio-control` using pip:
bash
pip install kling-motio-control

## Basic Usage

Here are a few examples showcasing the basic functionalities of the `kling-motio-control` library.

**1. Simulating a Basic Linear Motion:**
python
from kling_motio_control import motion

# Define motion parameters
start_position = 0
end_position = 100
duration = 5  # seconds

# Execute the motion
motion.linear_motion(start_position, end_position, duration)

print("Linear motion simulation complete.")

This example simulates a linear motion from position 0 to position 100 over a duration of 5 seconds. The `motion.linear_motion` function prints a simplified representation of the motion profile.

**2. Defining a Custom Motion Profile:**
python
from kling_motio_control import motion

# Define a custom motion profile as a list of (time, position) tuples
motion_profile = [(0, 0), (1, 25), (2, 50), (3, 75), (4, 90), (5, 100)]

# Execute the custom motion
motion.custom_motion(motion_profile)

print("Custom motion simulation complete.")

This example allows you to define a specific motion profile using a list of time-position pairs. The `motion.custom_motion` function simulates this custom movement.

**3. Simulating a Simple Robotic Arm Movement:**
python
from kling_motio_control import robotic_arm

# Define joint angles
joint1_angle = 30
joint2_angle = 45

# Simulate the arm movement
robotic_arm.move_arm(joint1_angle, joint2_angle)

print("Robotic arm movement simulation complete.")

This example simulates the movement of a simple robotic arm with two joints, allowing you to specify the angle for each joint.

**4. Integrating with Sensor Data (Example):**
python
from kling_motio_control import sensor_integration

# Simulate sensor data
sensor_value = 75

# Adjust motion based on sensor data
adjusted_speed = sensor_integration.adjust_speed(sensor_value)

print(f"Adjusted speed based on sensor data: {adjusted_speed}")

This example showcases how sensor data can be integrated to dynamically adjust motion parameters. The `sensor_integration.adjust_speed` function provides a simple demonstration of this capability.

**5. Visualizing Motion (Example):**
python
from kling_motio_control import visualization

# Sample data for plotting
x_data = [1, 2, 3, 4, 5]
y_data = [2, 4, 1, 3, 5]

# Create a simple plot
visualization.create_plot(x_data, y_data, "Time", "Position", "Motion Plot")

This demonstrates the included basic visualization capabilities, useful for understanding motion profiles.

## Feature List

*   **Linear Motion Simulation:** Simulates linear motion with customizable start position, end position, and duration.
*   **Custom Motion Profiles:** Allows defining and executing custom motion profiles using time-position pairs.
*   **Robotic Arm Simulation:** Simulates basic robotic arm movements with configurable joint angles.
*   **Sensor Data Integration:** Provides a framework for integrating sensor data to dynamically adjust motion parameters.
*   **Basic Visualization:** Offers simple plotting functionality to visualize motion profiles.
*   **Easy Installation:** Simple installation via pip.
*   **Modular Design:** Easy to extend and customize for specific applications.

## License

MIT License

This project is a gateway to the kling-motio-control ecosystem. For advanced features and full capabilities, please visit: https://supermaker.ai/blog/what-is-kling-motion-control-ai-how-to-use-motion-control-ai-free-online/