bmmliu/python-ervo

Reactive crowd simulator used in the IROS 2020 paper: "L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation"

Python-ERVO

• This repository containts the code for interactive crowd simulator used in the IROS 2020 paper "L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation"
• The original RVO implementation is the folk of Python bindings for Optimal Reciprocal Collision Avoidance.

Dependencies

• CMake 3.1+
• Cython 0.21.1+

Setup

$ pip install -r requirements.txt
$ python setup.py build      # build
$ python setup.py install    # build & install

Usage

• For more details, see example_ervo.py

# original RVO
sim_rvo = rvo2.PyRVOSimulator(time_step, *params, radius, max_speed)
sim_rvo.doStep()

# extended ERVO
sim_ervo = rvo2.PyERVOSimulator(time_step, *params, radius, max_speed)
sim_ervo.doStep((beep_agent.px, beep_agent.py), beep_radius)

Citation

If you find the simulator useful for your research, please consider citing:

@inproceedings{nishimura2020l2b,
  title={L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation},
  author={Nishimura, Mai and Yonetani, Ryo},
  journal={IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2020},
  publisher={IEEE}
}

@incollection{van2011reciprocal,
  title={Reciprocal n-body collision avoidance},
  author={Van Den Berg, Jur and Guy, Stephen J and Lin, Ming and Manocha, Dinesh},
  booktitle={Robotics research},
  pages={3--19},
  year={2011},
  publisher={Springer}
}