aimotioncontrol-net-oss 1.0.0

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

MotionTrajectory

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

Inspired by 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

go get github.com/user/aimotioncontrol-net-oss

Node.js

npm install aimotioncontrol-net-oss

Python

pip install aimotioncontrol-net-oss

Rust

cargo add aimotioncontrol-net-oss

Quick Start

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

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

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

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
• Node.js Documentation
• Python Documentation
• Rust Documentation

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see LICENSE for details.

Acknowledgments

• Inspired by the comprehensive AI motion control resources at aimotioncontrol.net
• Built with the open-source community