Motion Planning Networks

Implementation of MPNet: Motion Planning Networks. [arXiv]

The code can easily be adapted for Informed Neural Sampling.

Contains

• Data Generation
  • Any existing classical motion planner can be used to generate datasets. However, we provide following implementations in C++:
    • P-RRT*
    • RRT*
    • Example dataset: simple2D
• MPNet algorithm

Requirements

• Data Generation

  1. Install libbot2

     • Make sure all dependencies of libbot2 (e.g., lcm) are installed.
     • Install libbot2 with the local installation procedure.
     • Run "make" in the data_generation folder where the README file is located.

  2. Use any compiler such as Netbeans to load the precomplie code.

     • data_generation/src/rrts_main.cpp contains the main rrt/prrt code.

     • data_generation/viewer/src/viewer_main.cpp contains the visualization code.

       • Also checkout comments in data_generation/viewer/src/renderers/graph_renderer.cpp

     • Note: main_viewer and rrts_main should run in parallel as:

       • rrts_main sends the path solution as well as the tree to the main_viewer to publish through local network.
       • data is transmitted through LCM network protocol.

• MPNet

  • PyTorch

Examples

1. Assuming paths to obstacles point-cloud are declared, train obstacle-encoder: python MPNET/AE/CAE.py

2. Assuming paths to demonstration dataset and obstacle-encoder are declared, run mpnet_trainer:

   python MPNET/train.py

3. Run tests by first loading the trained models:

   python MPNET/neuralplanner.py

References

@inproceedings{qureshi2019motion,
  title={Motion planning networks},
  author={Qureshi, Ahmed H and Simeonov, Anthony and Bency, Mayur J and Yip, Michael C},
  booktitle={2019 International Conference on Robotics and Automation (ICRA)},
  pages={2118--2124},
  year={2019},
  organization={IEEE}
}
@inproceedings{qureshi2018deeply,
  title={Deeply Informed Neural Sampling for Robot Motion Planning},
  author={Qureshi, Ahmed H and Yip, Michael C},
  booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={6582--6588},
  year={2018},
  organization={IEEE}
}
@article{qureshi2019motion,
  title={Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners},
  author={Qureshi, Ahmed H and Miao, Yinglong and Simeonov, Anthony and Yip, Michael C},
  journal={arXiv preprint arXiv:1907.06013},
  year={2019}
}