https://github.com/siyuanwu99/ro47005-pdm-final
A C++ implementation of PRM path planning and mini-snap trajectory optimization | RO47005 Planning and Decision Making course project
https://github.com/siyuanwu99/ro47005-pdm-final
motion-planning path-planning quadrotor trajectory-optimization
Last synced: 6 months ago
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A C++ implementation of PRM path planning and mini-snap trajectory optimization | RO47005 Planning and Decision Making course project
- Host: GitHub
- URL: https://github.com/siyuanwu99/ro47005-pdm-final
- Owner: siyuanwu99
- License: mit
- Created: 2021-11-17T09:18:21.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-08-17T20:36:58.000Z (about 3 years ago)
- Last Synced: 2024-01-21T15:52:38.142Z (over 1 year ago)
- Topics: motion-planning, path-planning, quadrotor, trajectory-optimization
- Language: C++
- Homepage:
- Size: 4.81 MB
- Stars: 29
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# An implementation of k-PRM path planner with corridor-based trajectory optimization
This is course project of Group 17 for RO47005 Planning and Decision Making in TU Delft. We implemented k-PRM for front-end path searching and an iterative method for back-end corridor-based trajectory optimization.
## 1. Our contribution
- We implement occupancy map generation from scratch (please check `src/planner/map_server/`).
- We implement PRM planner and k-d tree from scratch .
- We implement A-star method for graph search from scratch (please check `src/planner/prm_planner/`).
- We implement classic minimum-snap trajectory optimization (both optimization solution and closed-form solution) from scratch (please check `src/planner/traj_opt/`).
- We implement corridor-based trajectory optimization from scratch (please check `src/planner/traj_opt/`). We use [OSQP_Interface](https://github.com/ZJU-FAST-Lab/OSQP_Interface) to solve QP problem. We also use [Root-Finder](https://github.com/ZJU-FAST-Lab/Root-Finder) to find tangent point of polynomial trajectory.
- The simulation environment (including simulator and controller) we used is cloned from [Fast-Planner](https://github.com/HKUST-Aerial-Robotics/Fast-Planner)
- We use [DecompROS](https://github.com/sikang/DecompROS) for safe flight corridor generation directly from occupancy map.
- We use [mockamap](https://github.com/HKUST-Aerial-Robotics/mockamap) with tiny modification to randomly generate point clouds and publish only once.
- We compared our PRM planner with [RRTs](https://github.com/medalotte/sampling-based-planners)
## 2. Build a demo
**Step 1**. Install Armadillo, which is required by `uav_simulator` package.
```
sudo apt-get install libarmadillo-dev
```
**Step 2**. Install [OSQP](https://github.com/osqp/osqp), which is required by `traj_opt` package.
**Step 3**. clone this repo
```
cd ~//src
git clone https://github.com/edmundwsy/RO47005-PDM-Final.git
cd ..
```
**Step 4**. Build the project
```
catkin_make
```
## 3. Run a simple demo
Load configuration files
```
source devel/setup.bash
```
### 3.1 Demo in random forest environment
Run following command to launch our project in rviz
```
roslaunch planner simulation.launch
```
Use 2D Nav Goal in rviz to publish a goal point
### 3.2 Demo in 3D Maze environment
Run following command to launch our project in rviz
```
roslaunch planner maze3d.launch
```
### 3.3 Compare with RRT
Run following command to launch rrt_planner
```
roslaunch rrt_planner simulation_with_map.launch
```
## Structure
```
├── ./planner
│ ├── ./planner/prm_planner
│ ├── ./planner/map_server
│ ├── ./planner/rrt_planner
│ ├── ./planner/traj_opt
│ └── ./planner/Utils
└── ./uav_simulator
├── ./uav_simulator/fake_drone
├── ./uav_simulator/local_sensing
├── ./uav_simulator/map_generator
├── ./uav_simulator/mockamap
├── ./uav_simulator/so3_control
├── ./uav_simulator/so3_quadrotor_simulator
└── ./uav_simulator/Utils
```
## License
Distributed under the MIT License. See `LICENSE` for more information.
## Contact
Siyuan Wu - S.Wu-14@student.tudelft.nl
Moji Shi - M.Shi-5@student.tudelft.nl
Ranbao Deng - R.Deng@student.tudelft.nl
Liangchen Sui - L.Sui@student.tudelft.nl
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[license-url]: https://github.com/edmundwsy/RO47005-PDM-Final/blob/master/LICENSE
[product-screenshot]: images/00.png