Giter Site home page Giter Site logo

phylliade / ikpy Goto Github PK

View Code? Open in Web Editor NEW
677.0 20.0 150.0 13.55 MB

An Inverse Kinematics library aiming performance and modularity

Home Page: http://phylliade.github.io/ikpy

License: Apache License 2.0

Python 100.00%
inverse-kinematics urdf robotics poppy python

ikpy's Introduction

IKPy

PyPI

DOI

demo

IKPy on the baxter robot

Demo

Live demos of what IKPy can do (click on the image below to see the video):

Also, a presentation of IKPy: Presentation.

Features

With IKPy, you can:

  • Compute the Inverse Kinematics of every existing robot.
  • Compute the Inverse Kinematics in position, orientation, or both
  • Define your kinematic chain using arbitrary representations: DH (Denavit–Hartenberg), URDF, custom...
  • Automatically import a kinematic chain from a URDF file.
  • Support for arbitrary joint types: revolute, prismatic and more to come in the future
  • Use pre-configured robots, such as baxter or the poppy-torso
  • IKPy is precise (up to 7 digits): the only limitation being your underlying model's precision, and fast: from 7 ms to 50 ms (depending on your precision) for a complete IK computation.
  • Plot your kinematic chain: no need to use a real robot (or a simulator) to test your algorithms!
  • Define your own Inverse Kinematics methods.
  • Utils to parse and analyze URDF files:

Moreover, IKPy is a pure-Python library: the install is a matter of seconds, and no compiling is required.

Installation

You have three options:

  1. From PyPI (recommended) - simply run:

    pip install ikpy

    If you intend to plot your robot, you can install the plotting dependencies (mainly matplotlib):

    pip install 'ikpy[plot]'
  2. From source - first download and extract the archive, then run:

    pip install ./

    NB: You must have the proper rights to execute this command

Quickstart

Follow this IPython notebook.

Guides and Tutorials

Go to the wiki. It should introduce you to the basic concepts of IKPy.

API Documentation

An extensive documentation of the API can be found here.

Dependencies and compatibility

Starting with IKPy v3.1, only Python 3 is supported. For versions before v3.1, the library can work with both versions of Python (2.7 and 3.x).

In terms of dependencies, it requires numpy and scipy.

sympy is highly recommended, for fast hybrid computations, that's why it is installed by default.

matplotlib is optional: it is used to plot your models (in 3D).

Contributing

IKPy is designed to be easily customisable: you can add your own IK methods or robot representations (such as DH-Parameters) using a dedicated developer API.

Contributions are welcome: if you have an awesome patented (but also open-source!) IK method, don't hesitate to propose adding it to the library!

Links

  • If performance is your main concern, aversive++ has an inverse kinematics module written in C++, which works the same way IKPy does.

Citation

If you use IKPy as part of a publication, please use the Bibtex below as a citation:

@software{manceron_pierre_2022_6551158,
  author       = {Manceron, Pierre},
  title        = {IKPy},
  month        = may,
  year         = 2022,
  note         = {{If you use this software, please cite it using the 
                   metadata from this file.}},
  publisher    = {Zenodo},
  version      = {v3.3.3},
  doi          = {10.5281/zenodo.6551158},
  url          = {https://doi.org/10.5281/zenodo.6551158}
}

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.