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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
UAV Motion-Planning Path-Planning A*, Kinodynamic A*, RRT, RRT*, SE(3)Planning, Minimum-Snap
| Date | Stars |
|---|---|
| 2026-07-24 | 403 |
| 2026-07-25 | 403 |
| 2026-07-28 | 403 |
| 2026-07-30 | 403 |
| 2026-08-06 | 403 |
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# UAV Motion Planning
## 0. Quick Installation within 3 Minutes
*Tested on ubuntu 20.04 LTS with ROS Noetic.*
1. Install [ROS](http://wiki.ros.org/ROS/Installation) (Desktop-Full Install *Recommended*).
2. Clone the repository.
```bash
git clone [email protected]:peiyu-cui/uav_motion_planning.git
```
3. Install dependences.
```bash
# eigen
sudo apt install libeigen3-dev
# osqp and osqp-eigen
cd uav_motion_planning
git submodule update --init --recursive
## osqp
cd 3rd/osqp
mkdir build
cd build
cmake .. # NOTE: if error occurs on cmake version, just change the cmake_minimum_required in the first line of CMakeLists.txt
make
sudo make install
## osqp-eigen
cd 3rd/osqp-eigen
mkdir build
cd build
cmake ..
make
sudo make install
```
4. Compile the code.
```bash
catkin_make -DCMAKE_CXX_STANDARD=14
```
## 1. Search-Based Methods
### 1.1. A*
- Quick start:
```bash
# in one terminal
source devel/setup.bash
roslaunch plan_manage single_run_in_sim.launch
# in another terminal
source devel/setup.bash
roslaunch test test_astar_searching.launch
```
- Parameters:
```xml
<!-- astar parameters -->
<param name="astar/resolution" value="0.1"/>
<param name="astar/lambda_heu" value="1.5"/>
<param name="astar/allocated_node_num" value="1000000"/>
```
- **astar/resolution:** astar search resolution, control the search resolution
- **astar/lambda_heu:** $f = g(n) + \lambda * h(n)$
- **astar/allocated_node_num:** pre-allocated search node num, avoid too many nodes
- Methods:
- tie_breaker: enhance the speed of searching
- weighted A*:
- Simulation:
### 1.2. Kinodynamic A*
- Quick start:
```bash
# in one terminal
source devel/setup.bash
roslaunch plan_manage single_run_in_sim.launch
# in another terminal
source devel/setup.bash
roslaunch test test_kino_astar_searching.launch
```
- Parameters: **NOTE: Some parameters need to change.**
- **kino_astar/collision_check_type:** 1: **kino_astar planning**, 2: **kino_se(3) planning**
- **(map_type change)** simulator.xml: **map/fix_map_type:** 0: generate random map, 1: generate fix wall map
```xml
<!-- kino_astar parameters -->
<param name="kino_astar/rou_time" value="20.0"/>
<param name="kino_astar/lambda_heu" value="3.0"/>
<param name="kino_astar/allocated_node_num" value="100000"/>
<param name="kino_astar/goal_tolerance" value="2.0"/>
<param name="kino_astar/time_step_size" value="0.075"/>
<param name="kino_astar/max_velocity" value="7.0"/>
<param name="kino_astar/max_accelration" value="10.0"/>
<param name="kino_astar/acc_resolution" value="4.0"/>
<param name="kino_astar/sample_tau" value="0.3"/>
<!-- collision check type 1: kino_astar, 2: kino_se3 -->
<param name="kino_astar/collision_check_type" value="2"/>
<!-- robot ellipsoid parameters -->
<param name="kino_se3/robot_r" value="0.4"/>
<param name="kino_se3/robot_h" value="0.1"/>
```
- Methods:
- **Paper:** B. Zhou, F. Gao, L. Wang, C. Liu and S. Shen, ["Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://arxiv.org/pdf/1907.01531)
- Simulation:
### 1.3. SE(3) Planning
- Quick start:
```bash
# in one terminal
source devel/setup.bash
roslaunch plan_manage single_run_in_sim.launch
# in another terminal
source devel/setup.bash
roslaunch test test_kino_astar_searching.launch
```
- Parameters: **NOTE: Some parameters need to change.**
- **kino_astar/collision_check_type:** 1: **kino_astar planning**, 2: **kino_se(3) planning**
- **(map_type change)** simulator.xml: **map/fix_Excerpt of 9,023 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f4e6dab74489febf, topic:ros, name:motion planning, desc:motion planning