dawnjeanh / motionplanning

Motion planning algorithm implementation

motionplanning

Motion planning algorithm implementation in Python and C++

Table of Contents

• C++ useage
• a* algorithm
• theta* algorithm
• Probabilistic Roadmaps algorithm
• Rapidly-exploring random tree algorithm
  • rrt
  • rrt_connect
  • rrt*
  • rrt*-smart
• Dubins path algorithm
  • shortest dubins path with different end point
  • 6 types of dubins path with same end point
  • RRT-Dubins
  • RRT*-Dubins
• Reeds Shepp path algorithm
  • shortest Reeds Shepp path with different end point
  • different types of Reeds Shepp path with same end point
  • Hybrid A* algorithm

C++ useage

C++11 standard, use Rviz to show algorithm, so you should install ROS.

1. put this package in your ros workspace, e.g. ~/catkin_ws/src/
2. cd ~/catkin_ws and catkin_make
3. source devel/setup.bash
4. launch script, e.g. roslaunch cpp_rviz a_star.launch

a* algorithm

theta* algorithm

Probabilistic Roadmaps algorithm

Rapidly-exploring random tree algorithm

rrt

rrt_connect

rrt*

rrt*-smart

Dubins path algorithm

Dubins algorithm have 6 types path as below.

shortest dubins path with different end point

6 types of dubins path with same end point

RRT-Dubins

RRT*-Dubins

Reeds Shepp path algorithm

The difference of Reeds Shepp path and Dubins path is that Reeds Sheep path have both forwards and backwards. So Dubins path is a subset of Reeds Shepp path.

shortest Reeds Shepp path with different end point

different types of Reeds Shepp path with same end point

Hybrid A* algorithm

based A* algorithm and Reeds Shepp path algorithm

motionplanning's Issues

Issue with one usecase

In hybridA* path planning I gave start point as (10,15,0) and end point as (46, 15, 0). Grid size has been increased to (60,60). But the path generated is not shortest. Instead of straight line Its making some curves. Can you please hep me?