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icra2018/grips

By icra2018

•Updated over 7 years ago

Gradient-Informed Path Smoothing for Wheeled Mobile Robots (GRIPS)

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icra2018/grips repository overview

⁠Gradient-Informed Path Smoothing for Wheeled Mobile Robots (GRIPS)

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C++ implementation of a post-smoothing approach that improves the quality of paths generated by sampling-based planners.

Planning smooth trajectories is important for the safe, efficient and comfortable operation of mobile robots, such as wheeled robots moving in crowded environments or cars moving at high speed. Asymptotically optimal sampling-based motion planners can be used to generate such trajectories eventually. However, to achieve the necessary efficiency for the real-time operation of robots, one often uses their initial feasible trajectories or the trajectories of non-optimal planners instead, typically after a post-smoothing step. We propose a gradient-informed post-smoothing algorithm, called GRIPS, that deforms given trajectories by locally optimizing the placement of vertices while satisfying the system's kinodynamic constraints. We show experimentally that GRIPS typically produces trajectories of significantly higher smoothness and smaller length than several existing post-smoothing algorithms.

If using GRIPS for scientific publications, please cite the following paper:

@inproceedings{heiden2018grips,
  author={Heiden, Eric and Palmieri, Luigi and Koenig, Sven and Arras, Kai O. and Sukhatme, Gaurav S.},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  title={Gradient-Informed Path Smoothing for Wheeled Mobile Robots},
  year={2018}
}

⁠Requirements

  • CMake >=3
  • Eigen 3
  • OMPL ~1.3.1⁠
  • Qt5 (ensure the Qt Charts and SVG packages are installed)

⁠Content

The following CMake targets are available:

CMake targetDescription
homotopy_testCompares paths from Theta* and A* before/after post-smoothing w.r.t homotopy class
benchmarkCompares different post-smoothing and path planning algorithms (cf. Table 1) and generates statistics JSON in log folder
shortening_testCompares path-shortening results on hand-crafted path (Fig. 2)
showcaseVisualizes post-smoothing of Theta* path in S-shaped environment (Fig. 3)

⁠Used third-party tools

⁠How to Run with Docker

⁠Linux

Tested on Ubuntu 16.04.6 with Docker 18.06.1-ce.

  1. Open a terminal and run the command:
docker run --rm -p 8888:8888 -e DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix  icra2018/grips:latest
  1. Run a web browser and open the link: http://localhost:8888/lab/tree/README.ipynb⁠

⁠macOS

⁠Prerequisites
  • XQuartz⁠:
    • Activate the option Allow connections from network clients in XQuartz settings
    • Quit & restart XQuartz (to activate the setting)
    • Open a terminal and run the command:
      xhost + localhost

Tested on macOS Mojave 10.14.3 with XQuartz 2.7.11 and Docker Desktop 2.0.0.3 (engine: 18.09.2).

  1. Open a terminal and run the command:
docker run --rm -p 8888:8888 -e DISPLAY=host.docker.internal:0 -v /tmp/.X11-unix:/tmp/.X11-unix  icra2018/grips:latest
  1. Run a web browser and open the link: http://localhost:8888/lab/tree/README.ipynb⁠

⁠Windows

⁠Prerequisites
  • VcXsrv⁠ or another X Windows Server
    • Run Xlaunch from the Start menu
    • Select Multiple windows, Start no client, and set Disable access control

Tested on Windows 10 Education wih VcXsrv 1.20.1.4 and Docker Desktop 2.0.0.3 (engine: 18.09.2).

  1. Open a Windows PowerShell and run the command:
docker run --rm -p 8888:8888 -e DISPLAY=host.docker.internal:0  icra2018/grips:latest
  1. Run a web browser and open the link: http://localhost:8888/lab/tree/README.ipynb⁠

Tag summary

Content type

Image

Digest

Size

311.6 MB

Last updated

over 7 years ago

docker pull icra2018/grips