# MotionTrajectory

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Go Version](https://img.shields.io/badge/Go-1.21+-00ADD8?logo=go)](https://golang.org/)
[![Node Version](https://img.shields.io/badge/Node.js-18+-green?logo=node.js)](https://nodejs.org/)
[![Python Version](https://img.shields.io/badge/Python-3.8+-blue?logo=python)](https://www.python.org/)
[![Rust Version](https://img.shields.io/badge/Rust-2021-orange?logo=rust)](https://www.rust-lang.org/)

> **AI-Powered Motion Trajectory Analysis Library**
> Extract, analyze, and optimize motion control patterns from trajectory data

Inspired by [aimotioncontrol.net](https://aimotioncontrol.net) - A comprehensive resource for AI motion control systems, robotics, and trajectory optimization.

## Overview

MotionTrajectory is a multi-language library for analyzing and optimizing motion control trajectories. It provides tools for:

- **Trajectory Extraction** - Parse motion data from various formats (CSV, JSON, binary)
- **Smoothing Algorithms** - Apply Kalman filtering, spline interpolation, and noise reduction
- **Kinematics Analysis** - Calculate velocity, acceleration, and jerk profiles
- **Path Optimization** - Generate time-optimal trajectories with constraint satisfaction
- **Motion Prediction** - Forecast future trajectory points using AI models

## Installation

### Go
```bash
go get github.com/user/aimotioncontrol-net-oss
```

### Node.js
```bash
npm install aimotioncontrol-net-oss
```

### Python
```bash
pip install aimotioncontrol-net-oss
```

### Rust
```bash
cargo add aimotioncontrol-net-oss
```

## Quick Start

### Go
```go
package main

import (
    "fmt"
    "github.com/user/aimotioncontrol-net-oss"
)

func main() {
    trajectory := motion.NewTrajectory([]motion.Point{
        {X: 0, Y: 0, Z: 0, Timestamp: 0},
        {X: 1, Y: 2, Z: 3, Timestamp: 100},
        {X: 2, Y: 4, Z: 6, Timestamp: 200},
    })

    smoothed := motion.ApplyKalmanFilter(trajectory, 0.1, 0.01)
    fmt.Printf("Smoothed trajectory: %+v\n", smoothed)
}
```

### Node.js
```javascript
const { TrajectoryAnalyzer, KalmanSmoother } = require('aimotioncontrol-net-oss');

const trajectory = [
    { x: 0, y: 0, z: 0, timestamp: 0 },
    { x: 1, y: 2, z: 3, timestamp: 100 },
    { x: 2, y: 4, z: 6, timestamp: 200 }
];

const analyzer = new TrajectoryAnalyzer(trajectory);
const smoothed = analyzer.applyKalmanFilter(0.1, 0.01);
console.log('Smoothed trajectory:', smoothed);
```

### Python
```python
from aimotioncontrol_net_oss import TrajectoryExtractor, Smoother

trajectory = [
    {"x": 0, "y": 0, "z": 0, "timestamp": 0},
    {"x": 1, "y": 2, "z": 3, "timestamp": 100},
    {"x": 2, "y": 4, "z": 6, "timestamp": 200}
]

extractor = TrajectoryExtractor()
smoother = Smoother()
smoothed = smoother.apply_kalman_filter(trajectory, process_noise=0.1, measurement_noise=0.01)
print(f"Smoothed trajectory: {smoothed}")
```

### Rust
```rust
use aimotioncontrol_net_oss::{Trajectory, Smoother};

fn main() {
    let trajectory = Trajectory::from(vec![
        (0.0, 0.0, 0.0, 0.0),
        (1.0, 2.0, 3.0, 100.0),
        (2.0, 4.0, 6.0, 200.0),
    ]);

    let smoother = Smoother::new();
    let smoothed = smoother.apply_kalman_filter(&trajectory, 0.1, 0.01);
    println!("Smoothed trajectory: {:?}", smoothed);
}
```

## Features

| Feature | Go | Node.js | Python | Rust |
|---------|----|---------|--------|------|
| Trajectory Parsing | ✅ | ✅ | ✅ | ✅ |
| Kalman Filtering | ✅ | ✅ | ✅ | ✅ |
| Spline Interpolation | ✅ | ✅ | ✅ | ✅ |
| Velocity/Acceleration | ✅ | ✅ | ✅ | ✅ |
| Path Optimization | ✅ | ✅ | ✅ | ✅ |

## API Documentation

- [Go Documentation](https://pkg.go.dev/github.com/user/aimotioncontrol-net-oss)
- [Node.js Documentation](https://www.npmjs.com/package/aimotioncontrol-net-oss)
- [Python Documentation](https://pypi.org/project/aimotioncontrol-net-oss/)
- [Rust Documentation](https://docs.rs/aimotioncontrol-net-oss/)

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

This project is licensed under the MIT License - see [LICENSE](LICENSE) for details.

## Acknowledgments

- Inspired by the comprehensive AI motion control resources at [aimotioncontrol.net](https://aimotioncontrol.net)
- Built with the open-source community
