cython rvo2 by Sybren Struval

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README.md

Python bindings for Optimal Reciprocal Collision Avoidance
This repository contains the RVO2 framework, as described below, along with Cython-based Python bindings. Its home is GitHub. New updates are released there. There are no explicit version numbers – all commits on the master branch are supposed to be stable.

Building & installing
Building requires CMake and Cython to be installed. Run pip install -r requirements.txt to install the tested version of Cython, or run pip install Cython to install the latest version.

Run python setup.py build to build, and python setup.py install to install. Alternatively, if you want an in-place build that puts the compiled library right in the current directory, run python setup.py build_ext --inplace

Only tested with Python 2.7, 3.4, and 3.6 on Ubuntu Linux. The setup.py script uses CMake to build the RVO2 library itself, before building the Python wrappers. If you have success (or failure) stories, please share them!

To build on Mac OSX, give an export MACOSX_DEPLOYMENT_TARGET=10.xx command first, before running python setup.py build. Replace 10.xx with your version of OSX, for example 10.11.

Differences with the C++ version
The Vector2 and Line classes from the RVO2 library are not wrapped. Instead, vectors are passed as tuples (x, y) from/to Python. Lines are passed as tuples (point x, point y, direction x, direction y).

Example code
#!/usr/bin/env python

import rvo2

sim = rvo2.PyRVOSimulator(1/60., 1.5, 5, 1.5, 2, 0.4, 2)

Pass either just the position (the other parameters then use

the default values passed to the PyRVOSimulator constructor),

or pass all available parameters.

a0 = sim.addAgent((0, 0))
a1 = sim.addAgent((1, 0))
a2 = sim.addAgent((1, 1))
a3 = sim.addAgent((0, 1), 1.5, 5, 1.5, 2, 0.4, 2, (0, 0))

Obstacles are also supported.

o1 = sim.addObstacle([(0.1, 0.1), (-0.1, 0.1), (-0.1, -0.1)])
sim.processObstacles()

sim.setAgentPrefVelocity(a0, (1, 1))
sim.setAgentPrefVelocity(a1, (-1, 1))
sim.setAgentPrefVelocity(a2, (-1, -1))
sim.setAgentPrefVelocity(a3, (1, -1))

print(‘Simulation has %i agents and %i obstacle vertices in it.’ %
(sim.getNumAgents(), sim.getNumObstacleVertices()))

print(‘Running simulation’)

for step in range(20):
sim.doStep()

positions = ['(%5.3f, %5.3f)' % sim.getAgentPosition(agent_no)
             for agent_no in (a0, a1, a2, a3)]
print('step=%2i  t=%.3f  %s' % (step, sim.getGlobalTime(), '  '.join(positions)))

Threading support
Calling Python-RVO2 from multiple threads has not been tested. However, code that may take longer to run (doStep(), processObstacles() and queryVisibility(…)) release the Global Interpreter Lock (GIL) so that other Python threads can run while RVO2 is processing.

Optimal Reciprocal Collision Avoidance
http://gamma.cs.unc.edu/RVO2/

Build Status Build status

Copyright 2008 University of North Carolina at Chapel Hill

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Please send all bug reports for the Python wrapper to Python-RVO2, and bug report for the RVO2 library itself to [email protected]

The RVO2 authors may be contacted via:

Jur van den Berg, Stephen J. Guy, Jamie Snape, Ming C. Lin, and Dinesh Manocha
Dept. of Computer Science
201 S. Columbia St.
Frederick P. Brooks, Jr. Computer Science Bldg.
Chapel Hill, N.C. 27599-3175
United States of America

Desktop version

what benefit gives this over the one built in to blender ?

you don’t need to use blender navmesh (you can’t update them in runtime etc)navmesh.update() = not working *

so if you use a jps or a* or any other algo you can combine it with this for obstacle avoidance.