Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Pinocchio | 993 | 8 | 6 | 18 hours ago | 5 | November 16, 2021 | 29 | other | C++ | |
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives | ||||||||||
Rigidbodydynamics.jl | 239 | 5 months ago | 32 | other | Julia | |||||
Julia implementation of various rigid body dynamics and kinematics algorithms | ||||||||||
Caliko | 120 | 2 years ago | 5 | mit | Java | |||||
The Caliko library is an implementation of the FABRIK inverse kinematics algorithm in Java. | ||||||||||
Everything Will Be Ik | 29 | 2 years ago | 4 | mit | HTML | |||||
A Robust Inverse Kinematics Library | ||||||||||
Urdf2casadi Matlab | 20 | 2 months ago | 4 | mit | MATLAB | |||||
Inverse Kinematics | 10 | 5 years ago | mit | C# | ||||||
During a research project, I came up with a pretty interesting algorithm for solving Inverse Kinematics problems iteratively. Check out the video! | ||||||||||
Zrobotics | 5 | 6 months ago | mit | Python | ||||||
A powerful library for robotics analysis 🤖 | ||||||||||
Calikocat | 5 | 9 months ago | other | CMake | ||||||
A C++ implementation of the FABRIK inverse kinematics algorithm, based on Caliko. | ||||||||||
Franka Robot Path Planning | 4 | 2 years ago | mit | Python | ||||||
Kinematics and RRT Path Planning algorithm for Franka Robot | ||||||||||
Fabrik Single Target | 4 | 6 years ago | C | |||||||
A C/SDL Inverse Kinematics demo. |
RigidBodyDynamics.jl is a rigid body dynamics library in pure Julia. It aims to be user friendly and performant, but also generic in the sense that the algorithms can be called with inputs of any (suitable) scalar types. This means that if fast numeric dynamics evaluations are required, a user can supply Float64
or Float32
inputs. However, if symbolic quantities are desired for analysis purposes, they can be obtained by calling the algorithms with e.g. SymPy.Sym
inputs. If gradients are required, e.g. the ForwardDiff.Dual
type, which implements forward-mode automatic differentiation, can be used.
See the latest stable documentation for a list of features, installation instructions, and a quick-start guide. Installation should only take a couple of minutes, including installing Julia itself. The documentation includes various usage examples, starting with a quickstart guide. These examples are also runnable locally as Jupyter notebooks; see the readme in the examples directory for instructions.
RigidBodyDynamics.jl is part of the JuliaRobotics GitHub organization.
Packages built on top of RigidBodyDynamics.jl include:
Mechanism
s using Director.