/safe-interactive-crowdnav

Code base for SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization

Primary LanguagePythonMIT LicenseMIT

Safe and Interactive Crowd Navigation

This repository contains the code for our paper SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization including the CrowdSimPlus Simulator.

Website | Repo | Paper | Video

@misc{samavi2023sicnav,
      title={SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization},
      author={Sepehr Samavi and James R. Han and Florian Shkurti and Angela P. Schoellig},
      year={2023},
      eprint={2310.10982},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      note={Currently under Review for the IEEE Transactions on Robotics (T-RO).}
}

Setup

In a conda environment with Python 3.8.13,

  1. Install this package using pip:

    pip install -e .

  2. Install the required libraries:

    pip install -r requirements.txt

  3. Clone and install Python-RVO2 library. Note that a compatible version of Cython 0.29.33 will be installed already in step 2. You should only need to run:

    pip install -e <path-to-Python-RVO2-dir>/Python-RVO2/

  4. (Recommended) Install HSL to use advanced solvers in IPOPT, the settings that we use in campc.py make use of a linear solver from HSL, however the code in this repo will work with the default IPOPT settings as well. Instructions: https://github.com/casadi/casadi/wiki/Obtaining-HSL.

Testing Crowd Navigation Algorithms

The following algorithms can be visualized by running the following command in the sicnav/ directory:

python simple_test.py --policy <policy>

  • Collision Avoidance MPC (--policy campc) can be configured in sicnav/configs/policy.config to act as the following algorithms described in the paper:

    • SICNav-p (ours): our algorithm with access to privileged information about the humans
    • SICNav-np (ours): our algorithm without access to privileged information about the humans
    • MPC-CVMM: the MPC baseline that does not model human interactively

  • Dynamic Window Approach (--policy dwa)

To train and visualize the Reinforcement Learning algorithms, SARL and RGL, please see the RL_nav/ subdirectory.

CrowdSimPlus Simulator

CrowdSimPlus is based on OpenAI gym and is an extension of CrowdSim (Repo, Paper), with the follwing added features:

  • Static obstacles to allow for more realistic dense scenarios.
  • Social Forces Model (SFM) policy to simulate human agents, in addition to the original Optimal Reciprocal Collision Avoidance (ORCA) policy. These policies can be found under crowd_sim_plus.envs.policy.
  • Stable Baselines 3 (SB3) integration for Reinforcement Learning (RL) methods.

Contributors

This repository is primarily developed by Sepehr Samavi (sicnav and crowd_sim_plus) and James R. Han (RL_nav and crowd_sim_plus).