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

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 <TEX>$tree^*$</TEX> (<TEX>$RRT^*$</TEX>) 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 <TEX>$RRT^*$</TEX> which is an extended version of <TEX>$RRT^*$</TEX> to increase the rate of convergence to optimal solution by improving the sampling method of <TEX>$RRT^*$</TEX>. 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 <TEX>$RRT^*$</TEX> by combining with the sampling method to improve the path nearby robot. With comparison among basic <TEX>$RRT^*$</TEX>, informed <TEX>$RRT^*$</TEX> and the proposed <TEX>$RRT^*$</TEX> in online motion planning, the proposed <TEX>$RRT^*$</TEX> showed the best result by representing the closest solution to optimum.