dynotree 0.0.4

Dynotree - Dynamic Kd tree

dyn_kdtree

Dynamic Kd-Tree: Euclidean, SO(2), SO(3) and more! C++ with Python Bindings

Dynamic KD-Tree: Euclidean, SO(2), SO(3), and more! C++ with Python Bindings

The underlying KD-tree implementation is based on bucket-pr-kdtree, but dynotree supports non Euclidean spaces and custom componed spaces. State spaces can be defined at both compile (effiency) and runtime( flexibility).

Try it out!

PYTHON

pip3 install dynotree

A first example: rrt_free.py

A second example: rrt.py

C++

Test and examples are in main.cpp

git clone --recurse-submodules https://github.com/quimortiz/dyn_kdtree
cd dyn_kdtree
mkdir build
cd build
cmake ..
make
./main

use

./main --run_test=NAME_OF_TEST

to run only one example or test

DEV

Testing

Create wheels

for publishing in pypy

docker pull quay.io/pypa/manylinux2014_x86_64
docker run -it -v $(pwd):/io quay.io/pypa/manylinux2014_x86_64 /io/build-wheels.sh
python3 -m twine upload wheelhouse/*

using testpypi

python3 -m twine upload --repository testpypi wheelhouse/*

for creating local package:

TODO

Why dyn_kdtree?

• Faster than OMPL and simpler than NIGH
• Dynamic: Add points one by one -- Ideal for Motion Planning
• Support Euclidean, SO(2), SO(3) and any combination
• Performant and flexible C++ code based on Eigen.
• Single Header File
• Python Bindings for easy integration
• Extendable with custom spaces!

Python Bindings

Option 1

Option 2

Option 3

Dependencies

Python: No dependencies

C++ Code: Eigen

Develop: Eigen and Boost Testing

Interface

See this for examples.

Benchmark

Check this repo for benchmark against

• bucket-pr-kdtree
• OMPL
• Nigh
• ...

Roadmap

Code is Stable, currently used in ...