Improvement of RRT*-Smart Algorithm for Optimal Path Planning and Application of the Algorithm in 2 & 3-Dimension Environment

Abstract

Optimal path planning refers to find the safe route to the destination at a low cost, is a major problem with regard to autonomous navigation. Sampling Based Planning(SBP) approaches, such as Rapidly-exploring Random Tree Star(RRT*), are the most influential algorithm in path planning due to their relatively small calculations and scalability to high-dimensional problems. RRT*-Smart introduced path optimization and biased sampling techniques into RRT* to increase convergent rate. This paper presents an improvement plan that has changed the biased sampling method to increase the initial convergent rate of the RRT*-Smart, which is specified as mRRT*-Smart. With comparison among RRT*, RRT*-Smart and mRRT*-Smart in 2 & 3-D environments, mRRT*-Smart showed similar or increased initial convergent rate than RRT* and RRT*-Smart.