mit-acl / puma

PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment

PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment

Submitted to 2024 IEEE International Conference on Robotics and Automation (ICRA)

YouTube Video

https://www.youtube.com/watch?v=W73p42XRcaQ

Citation

(ICRA24 Paper) PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment:

@article{kondo2023puma,
  title={{PUMA}: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment},
  author={Kondo, Kota and Tewari T. Claudius and Peterson, B. Mason and Thomas, Annika and Kinnari, Jouko and Tagliabue, Andrea and How, Jonathan P},
  journal={arXiv preprint arXiv:2311.03655},
  year={2023}
}

Using Docker

Our uncertainty-aware planner uses IPOPT and HSL packages. To use HSL packages, you need to go to http://www.hsl.rl.ac.uk/ipopt/, click on HSL Academic Licence (if you are in an academic institute) to install the solver MA27 (free for everyone), and submit the form. Once you receive the corresponding email, download the compressed file, uncompress it (eg. coinhsl-2015.06.23), and place it in the folder puma/puma/docker.

PUMA

To run uncertainty-aware planner with one dynamic obstacle, run the following script.

roscd puma && cd docker
make build-planner
make run-planner

Image Segmentation-based Real-time Frame Alignment

To run frame alignment pipeline, run the following script.

roscd puma && cd docker
make build-frame
make run-frame

Multiagent PUMA on Segmentation-based Real-time Frame Alignment

To run multiagent PUMA on frame alignment pipeline, run the following script.

roscd puma && cd docker
make build-multiagent
make run-multiagent

Note

If you see the following error message, you need to run xhost +local:docker on your host machine.

No protocol specified
qt.qpa.xcb: could not connect to display :1
qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

NOT USING DOCKER

PUMA has been tested with Ubuntu 20.04/ROS Noetic.

PUMA

To set up an environment for PUMA, run the following script.

./install_puma_deps.bash

Image Segmentation-based Real-time Frame Alignment

To set up an environment for the frame alignment pipeline, run the following script.

./install_fastsam_deps.bash

Demos

PUMA has been tested with Ubuntu 20.04/ROS Noetic. Other Ubuntu/ROS version may need some minor modifications, feel free to create an issue if you have any problems.

The python scripts described below use tmux, and if you want to see what is going on in the background, use tmux attach.

PUMA

roscd puma && cd other/demos
python3 uncertainty_aware_planner_demo.py

• uncertainty_aware_planner_demo.py runs our uncertainty-aware planner with one dynamic obstacle and visualize it in RViz.
• If you want to change parameters of the planner, you can take a look at puma.yaml in the param folder.
• If you want to change the planner's optimization formulation, you can take a look at main.m in the matlab folder.
• Note that PUMA is still computationally heavy, and therefore we pause the ROS time while solving for the optimal trajectory -- you can change this in pause_time_when_replanning in puma.yaml.

Image Segmentation-based Real-time Frame Alignment

roscd puma && cd other/demos
python3 frame_alignment_demo.py

• frame_alignment_demo.py runs our frame alignment algorithm and visualize it in RViz.
• If you want to record a bag, pass True to --record_bag and specify where to save a rosbag in --output_dir.
• If you don't have CUDA on your computer, change self.DEVICE in fastsam.py to cpu.

Multiagent PUMA on Segmentation-based Real-time Frame Alignment

roscd puma && cd other/demos
python3 uncertaintyaware_planner_on_frame_alignment_demo.py

• Note that PUMA is still computationally heavy, and therefore we pause the ROS time while solving for the optimal trajectory -- you can change this in pause_time_when_replanning in puma.yaml.
• Currently, main.m supports obstacle tracking and uncertainty propagation for one obstacle/agent; however, Check and DelayCheck in Robust MADER's trajectory deconfliction checks potential for all the received trajectories so PUMA guarantees safety.

Important files

If you want to...

• Tune PUMA's cost functions: main.m
  • Required matlab add-ons: Phased Array System Toolbox, Statistics and Machine Learning Toolbox, Symbolic Math Toolbox
  • PUMA is develped on MATLAB R2022b -- symvar related error on MATLAB R2023b.
• Take a look at how we implemented FastSAM: fastsam.py.
• Modify the optimization problem: You will need to have MATLAB installed (especifically, you will need the Symbolic Math Toolbox and the Phased Array System Toolbox installed), and follow the steps detailed in the MATLAB section below. You can then make any modification in the optimization problem by modifying the file main.m, and then running it. This will generate all the necessary .casadi files in the casadi_generated_files folder, which will be read by the C++ code.

puma's Issues

Unable to obtain the reference project of gitlab

Hello, the dependent projects in the gitlab cannot be obtained during the running of the project. How can I obtain those projects?