# puma **Repository Path**: daoos_admin/puma ## Basic Information - **Project Name**: puma - **Description**: 多智能体动态路径规划 - **Primary Language**: Unknown - **License**: BSD-3-Clause - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-11-23 - **Last Updated**: 2023-11-23 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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)**

Image segmentation-based real-time frame alignment pipeline (pads, circle, constant drifts)

Image segmentation-based real-time frame alignment pipeline (pads, partically overlapping circle, linear drfits)

Image segmentation-based real-time frame alignment pipeline with PUMA (random objects, linear drifts)

Hardware experiments: image segmentation-based real-time frame alignment pipeline (pads, circle)

## YouTube Video [https://www.youtube.com/watch?v=W73p42XRcaQ](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](https://arxiv.org/abs/2311.03655): ```bibtex @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} } ``` ## Setup 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](https://github.com/mit-acl/puma/issues) 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](https://github.com/mit-acl/rmader)'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.