A revised Gaussian distribution sampling scheme based on RRT* algorithms in robot motion planning
Yuncheng Li, Jie Shao · 2017
This paper introduces the basic Rapidly-Exploring Random Tree (RRT) and its basic modification Rapidly-Exploring Random Tree star (RRT*), which is not only the extension of RRT, but also a widely applied algorithm because of the properties of asymptotically optimal path regardless of any obstacles, whereas the limitation to achieve optimal path has a slow convergence rate. As a result, it costs too much memory and time due to a large number of iterations, so we propose a method that should change the sampling scheme from random distribution sampling to Gaussian distribution sampling to overcome this limitation. In order to apply the improved algorithm in robot arms or manipulators motion planning, we extend the RRT* to simulate in higher dimensional spaces, the planner is implemented in 3D workspace. Finally we also revise the Gaussian distribution to suit the practical environment.