Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

Abstract

This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments