NAMOSIM

NAMOSIM is a robot motion planner designed for the problem of navigation among movable obstacles (NAMO). It simulates mobile robots navigating in 2D polygonal environments in which certain obstacles can be grabbed and relocated. It currently supports holonomic and differential drive motion models. A variety of agent types are implemented, including primarily our Stilman2005 baseline agent. New agents utilizing alternative algorithmic approaches can be created and plugged into the planner in a straightforward manner by implementing the Agent base class.

Documentation

Please check out the docs site for installation and usage instructions.

To build the docs site locally, run:

./scripts/make_docs.sh

Demos

Here are a couple demo videos applying namosim on real and simulated robots.

NAMOSIM on a Turtlebo

NAMOSIM on Multiple Robots in Gazebo

Authors

• Benoit Renault
• Jacques Saraydaryan
• David Brown
• Olivier Simonin

Affiliated Teams and Organisations

	Org/Team
	Inria
	INSA Lyon
	CITI Laboratory
CHROMA	CHROMA Team

Cite Us

If you reuse any part of this project in your research, please cite the associated papers:

@inproceedings{renault:hal-04705395,
  TITLE = {{Multi-Robot Navigation among Movable Obstacles: Implicit Coordination to Deal with Conflicts and Deadlocks}},
  AUTHOR = {Renault, Benoit and Saraydaryan, Jacques and Brown, David and Simonin, Olivier},
  URL = {https://hal.science/hal-04705395},
  BOOKTITLE = {{IROS 2024 - IEEE/RSJ International Conference on Intelligent Robots and Systems}},
  ADDRESS = {Abu DHABI, United Arab Emirates},
  PUBLISHER = {{IEEE}},
  PAGES = {1-7},
  YEAR = {2024},
  MONTH = Oct,
  KEYWORDS = {Planning ; Scheduling and Coordination ; Path Planning for Multiple Mobile Robots or Agents ; Multi-Robot Systems},
  PDF = {https://hal.science/hal-04705395v1/file/IROS24_1134_FI.pdf},
  HAL_ID = {hal-04705395},
  HAL_VERSION = {v1},
}

@inproceedings{renault:hal-02912925,
  TITLE = {{Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms}},
  AUTHOR = {Renault, Benoit and Saraydaryan, Jacques and Simonin, Olivier},
  URL = {https://hal.archives-ouvertes.fr/hal-02912925},
  BOOKTITLE = {{IROS 2020 - IEEE/RSJ International Conference on Intelligent Robots and Systems}},
  ADDRESS = {Las Vegas, United States},
  SERIES = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Conference Proceedings},
  PAGES = {11345-11351},
  YEAR = {2020},
  MONTH = Oct,
  DOI = {10.1109/IROS45743.2020.9340892},
  KEYWORDS = {Navigation Among Movable Obstacles (NAMO) ; Socially- Aware Navigation (SAN) ; Path planning ; Simulation},
  PDF = {https://hal.archives-ouvertes.fr/hal-02912925/file/IROS_2020_Camera_Ready.pdf},
  HAL_ID = {hal-02912925},
  HAL_VERSION = {v1},
}

Changelog for package namosim

0.0.1 (2025-08-26)

• Merge pull request #12 from Chroma-CITI/rm_torch Update instructions
• docs
• Merge pull request #11 from Chroma-CITI/rm_torch remove dependency on pytorch
• typo
• fix circular import
• remove imports of ppo agent
• remove imports of ppo agent
• remove imports of ppo agent
• remove dependency on pytorch
• add jacques as maintainer
• ci
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• ci
• skip unit tests for colcon test
• rm pip deps from package.xml
• move pip packages to exec_depend
• ci
• ci
• show colcon test output
• remove dependency on aabbtree
• fix dependencies and colcon test
• add draft-pdf workflow
• docs
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• add orcid ids
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• fix citations
• docs
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• docs and ci
• ci
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• update github action
• Merge branch 'humble' of github.com:Chroma-CITI/namosim into humble
• update github action
• Merge pull request #1 from Chroma-CITI/paper Paper
• update github action
• add github action
• add github action
• paper
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• Merge branch 'joss' into 'humble' add joss paper See merge request chroma/namo/namosim!196
• add joss paper
• Merge branch 'three_robots' into 'humble' Three robots See merge request chroma/namo/namosim!194
• use absolute cost values when compuing evasion cost
• is_for_deadlock
• fix tests
• format
• types
• fix types
• clean up
• clean up
• clean up
• simplify conflict detection
• simplifying conflict detection
• clean up
• clean up

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