Improvement of Online Motion Planning based on RRT* by Modification of the Sampling Method

Title & Authors
Improvement of Online Motion Planning based on RRT* by Modification of the Sampling Method
Lee, Hee Beom; Kwak, HwyKuen; Kim, JoonWon; Lee, ChoonWoo; Kim, H.Jin;

Abstract
Motion planning problem is still one of the important issues in robotic applications. In many real-time motion planning problems, it is advisable to find a feasible solution quickly and improve the found solution toward the optimal one before the previously-arranged motion plan ends. For such reasons, sampling-based approaches are becoming popular for real-time application. Especially the use of a rapidly exploring random $\small{tree^*}$ ($\small{RRT^*}$) algorithm is attractive in real-time application, because it is possible to approach an optimal solution by iterating itself. This paper presents a modified version of informed $\small{RRT^*}$ which is an extended version of $\small{RRT^*}$ to increase the rate of convergence to optimal solution by improving the sampling method of $\small{RRT^*}$. In online motion planning, the robot plans a path while simultaneously moving along the planned path. Therefore, the part of the path near the robot is less likely to be sampled extensively. For a better solution in online motion planning, we modified the sampling method of informed $\small{RRT^*}$ by combining with the sampling method to improve the path nearby robot. With comparison among basic $\small{RRT^*}$, informed $\small{RRT^*}$ and the proposed $\small{RRT^*}$ in online motion planning, the proposed $\small{RRT^*}$ showed the best result by representing the closest solution to optimum.
Keywords
online motion planning;$\small{RRT^*}$;$\small{RRT^*}$ sampling method;
Language
Korean
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