Implementation of multi-robot-fabrics presented in our MRS 2023 paper "Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics"
In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high success rates and real-time performance.
A video showcasing the presented approach can be found here.
The current version of the paper can be cited using the following reference:
@inproceedings{bakker2023multi,
title={Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics},
author={Bakker, Saray and Knoedler, Luzia and Spahn, Max and B{\"o}hmer, Wendelin and Alonso-Mora, Javier},
booktitle={2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},
pages={149--155},
year={2023},
organization={IEEE}
}This repository is meant to explore the use of fabrics for multiple mobile robots/robotic manipulators. Several flavors are explored:
- Fabrics applied to multi-robot scenarios.
- Rollout fabrics applied to multi-robot scenarios. (Forward predictions over a horizon to detect future deadlocks or other undesired states)
The 'examples' folder provides runnable examples of different scenarios.
This repository includes examples of the application of multi-robot fabrics to point robots and Panda robotic arms. The examples can be run
- without rollouts (in the paper referred to as MRDF),
- with rollout fabrics and deadlock resolution heuristic (in the paper referred to as RF), and
- with rollout fabrics, constant velocity goal estimation and deadlock resolution (in the paper referred to as RF-CV)
While dynamic fabrics (Spahn2023) are applied in the paper, we also support static fabrics as introduced in Ratliff2020. The point-robot example was added for easy understanding and does currently not provide support for rollout fabrics and deadlock resolution.
Which configuration is used can be accessed in examples/configs. Here, also the number of robots and the number of collision spheres can be adapted.
Further parameters can be adapted in 'parameters_manipulators.py'.
| Point Robot | 2 Panda Scenario | 3 Panda Scenario |
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Clone this repository and go to its root:
git clone git@github.com:tud-amr/multi-robot-fabrics.git
cd multi-robot-fabrics
You can install the package using poetry. For more details on poetry see installation instructions.
poetry install
The requirements can be found in pyproject.toml.
Enter the virtual environment using:
poetry shell
In the folder multi_robot_fabrics run
python examples/<example-file-name>
E.g. to run the panda example python examples/example_pandas_Jointspace.py.
If you run into problems of any kind or have a question, do not hesitate to open an issue on this repository. Or have a look at the tips we summarized here.
