WRS JLChain Native
Native FK, Jacobian, IK, and runtime-state acceleration for WRS
JLChain.
The package keeps the WRS Python API intact while moving the hot kinematics work into a C++ backend. The first backend is implemented with Orocos KDL; the Python adapter is structured so additional backends such as Pinocchio or RBDL can be added later without changing robot code.
Package Names
- GitHub repository:
wrs-jlchain-native - PyPI package:
wrs-jlchain-native - Python import package:
wrs_jlchain_native - Native extension:
_wrs_jlchain_native_kdl
Requirements
- Python 3.10 or newer
- NumPy
- A WRS checkout when using the accelerated drop-in
JLChain - A C++17 compiler for source builds
- CMake and pybind11 for source builds
Wheel builds use includeigen to provide Eigen headers and build the bundled
Orocos KDL source from third_party/orocos_kinematics_dynamics.
Repository Layout
src/
CMakeLists.txt
python/wrs_jlchain_native/
__init__.py
backends.py
jlchain.py
wrs_jlchain_native/
kdl_backend.cpp
python_bindings.cpp
include/wrs_jlchain_native/
backend.hpp
kdl_backend.hpp
Install From PyPI
After publishing:
python -m pip install wrs-jlchain-native
Then use the native-backed chain directly:
from wrs_jlchain_native import JLChain
jlc = JLChain(n_dof=6, backend_name="kdl")
jlc.finalize()
This package is intentionally kept outside the WRS source tree. To integrate it
with a WRS robot class, import wrs_jlchain_native.JLChain in the WRS-side
module that constructs the robot's kinematic chain, or add a small compatibility
shim in your WRS checkout that aliases this class.
from wrs_jlchain_native import JLChain as NativeJLChain
chain = NativeJLChain(n_dof=6, backend_name="kdl")
Runtime Selection
Per chain:
chain = JLChain(n_dof=6, backend_name="kdl")
Globally:
$env:WRS_JLCHAIN_BACKEND="kdl"
Supported values:
auto: try the native backend and fall back to Pythonkdl,orocos,orocos_kdl: require the Orocos KDL backendpython,none,off: force the original Python implementation
Build From Source
Initialize submodules first:
git submodule update --init --recursive
Build a wheel:
python -m pip install build
python -m build --sdist --wheel
Manual CMake build:
cmake -S src -B build\jlchain_native `
-DCMAKE_PREFIX_PATH=D:\code\venv312\.venv\Lib\site-packages\pybind11\share\cmake\pybind11 `
-DPYTHON_EXECUTABLE=D:\code\venv312\.venv\Scripts\python.exe
cmake --build build\jlchain_native --config Release
If Eigen is not discovered through includeigen, pass it explicitly:
-DEIGEN3_INCLUDE_DIR=D:\code\vcpkg\packages\eigen3_x64-windows\include\eigen3
For local WRS testing without installing a wheel, put the build output on
PYTHONPATH:
$env:PYTHONPATH="D:\code\c_implementation_backend\build\jlchain_native\Release;$env:PYTHONPATH"
Benchmark
The benchmark used during development lives in the separate WRS checkout and is
not committed to this standalone package repository. From a WRS workspace that
contains that benchmark, add the native build output or installed wheel to
PYTHONPATH and run:
D:\code\venv312\.venv\Scripts\python.exe wrs\0000_test_programs\jlchain_speed_benchmark.py
It reports FK, FK plus Jacobian, direct Jacobian, FK state update, update plus Jacobian, and IK timings, along with numerical error checks.
CI And Publishing
GitHub Actions workflows are in .github/workflows:
ci.yml: builds source distribution and wheels on pull requests and pushespublish.yml: builds distributions and publishes to PyPI on GitHub releases
PyPI publishing uses Trusted Publishing. Configure the PyPI publisher with:
- repository:
<owner>/wrs-jlchain-native - workflow:
publish.yml - environment:
pypi
License
This package links against/builds Orocos KDL, which is LGPL-2.1-or-later. The
package metadata therefore uses LGPL-2.1-or-later.
1 maintainer
Metadata
1 maintainer
Release files for wrs-jlchain-native 0.1.0
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